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1
Spencer, Matthew. "Evolving complex network models of functional connectivity dynamics." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590143.
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Functional connectivity networks describe how regions of the brain interact. The timing, location, and frequency of these interactions inform about memory, decision making, motor movement, affective states, and more. However, while these interactions are well described as networks, these networks, like many others throughout nature, are constantly changing. Complex network evolution poses a highly dimensional problem but also contains much information about the system in question. In this thesis, a recent class of evolving complex network models was explored and extended to capture the functional connectivity dynamics observed in neuronal networks. Functional connectivity was investigated through data- and model-driven techniques at the cellular level, with cultures of cortical neurones on multi-electrode arrays, and at the whole-brain level, with electroencephalography. At the neuronal level, complex spatial dependencies were identified in bursts of excitation and two novel network models, the Starburst model and the Excitation Flow model, are used to capture the resulting functional connectivity. At the whole-brain level, functional connectivity dynamics were used to perform single-trial classification of intentional motor movement. Again, spatiotemporal dependencies were identified and used to present three novel techniques for modelling the network dynamics. The first two techniques decomposed networks into network templates (one model-based and one spectral-based) and modelled the dynamics with hidden Markov models. The final technique was a generalised evolving version of the Starburst model. The hidden Markov model of spectrally decomposed networks was shown to classify motor intentions with an accuracy around 80%. Firstly, this thesis shows that time plays an important role in the production of the complex network topologies observed in functional connectivity, both at the cellular and whole-brain leve1. Further, it is shown that evolving complex network models are very useful tools for modelling these topologies and that the network dynamics can be used to uncover features that are crucial to identifying functional states.
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Preciado, Víctor Manuel. "Spectral analysis for stochastic models of large-scale complex dynamical networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45873.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 179-196).
Research on large-scale complex networks has important applications in diverse systems of current interest, including the Internet, the World-Wide Web, social, biological, and chemical networks. The growing availability of massive databases, computing facilities, and reliable data analysis tools has provided a powerful framework to explore structural properties of such real-world networks. However, one cannot efficiently retrieve and store the exact or full topology for many large-scale networks. As an alternative, several stochastic network models have been proposed that attempt to capture essential characteristics of such complex topologies. Network researchers then use these stochastic models to generate topologies similar to the complex network of interest and use these topologies to test, for example, the behavior of dynamical processes in the network. In general, the topological properties of a network are not directly evident in the behavior of dynamical processes running on it. On the other hand, the eigenvalue spectra of certain matricial representations of the network topology do relate quite directly to the behavior of many dynamical processes of interest, such as random walks, Markov processes, virus/rumor spreading, or synchronization of oscillators in a network. This thesis studies spectral properties of popular stochastic network models proposed in recent years. In particular, we develop several methods to determine or estimate the spectral moments of these models. We also present a variety of techniques to extract relevant spectral information from a finite sequence of spectral moments. A range of numerical examples throughout the thesis confirms the efficacy of our approach. Our ultimate objective is to use such results to understand and predict the behavior of dynamical processes taking place in large-scale networks.
by Víctor Manuel Preciado.
Ph.D.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-89260.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Max-Planck-Institut für Physik komplexer Systeme, 2011. https://tud.qucosa.de/id/qucosa%3A26056.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
5
Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.
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Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
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Peron, Thomas Kauê Dal\'Maso. "Dynamics of Kuramoto oscillators in complex networks." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-21092017-100820/.
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Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from biological and physical to social and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. For decades, this model has been traditionally studied in globally coupled topologies. However, besides being intrinsically dynamical, complex systems exhibit very heterogeneous structure, which can be represented as complex networks. This thesis is dedicated to the investigation of fundamental problems regarding the collective dynamics of Kuramoto oscillators coupled in complex networks. First, we address the effects on network dynamics caused by the presence of triangles, which are structural patterns that permeate real-world networks but are absent in random models. By extending the heterogeneous degree mean-field approach to a class of configuration model that generates random networks with variable clustering, we show that triangles weakly affect the onset of synchronization. Our results suggest that, at least in the low clustering regime, the dynamics of clustered networks are accurately described by tree-based theories. Secondly, we analyze the influence of inertia in the phases evolutions. More precisely, we substantially extend the mean-field calculations to second-order Kuramoto oscillators in uncorrelated networks. Thereby hysteretic transitions of the order parameter are predicted with good agreement with simulations. Effects of degree-degree correlations are also numerically scrutinized. In particular, we find an interesting dynamical equivalence between variations in assortativity and damping coefficients. Potential implications to real-world applications are discussed. Finally, we tackle the problem of two intertwined populations of stochastic oscillators subjected to asymmetric attractive and repulsive couplings. By employing the Gaussian approximation technique we derive a reduced set of ODEs whereby a thorough bifurcation analysis is performed revealing a rich phase diagram. Precisely, besides incoherence and partial synchronization, peculiar states are uncovered in which two clusters of oscillators emerge. If the phase lag between these clusters lies between zero and π, a spontaneous drift different from the natural rhythm of oscillation emerges. Similar dynamical patterns are found in chaotic oscillators under analogous couplings schemes.Sincronização de conjuntos de osciladores é um fenômeno emergente que permeia sistemas complexos de diversas naturezas, como por exemplo, sistemas biológicos, físicos, naturais e tecnológicos. A abordagem mais bem sucedida na descrição da emergência de comportamento coletivo em sistemas complexos é fornecida pelo modelo de Kuramoto. Durante décadas, este modelo foi tradicionalmente estudado em topologias completamente conectadas. Entretanto, além de ser intrinsecamente dinâmicos, tais sistemas complexos possuem uma estrutura altamente heterogênea que pode ser apropriadamente representada por redes complexas. Esta tese é dedicada à investigação de problemas fundamentais da dinâmica coletiva de osciladores de Kuramoto acoplados em redes. Primeiramente, abordamos os efeitos sobre a dinâmica das redes causados pela presença de triângulos padrões que estão omnipresentes em redes reais mas estão ausentes em redes gerados por modelos aleatórios. Estendemos a abordagem via campo-médio para uma variação do modelo de configuração tradicional capaz de criar topologias com número variável de triângulos. Através desta abordagem, mostramos que tais padrões estruturais pouco influenciam a emergência de comportamento coletivo em redes, podendo a dinâmica destas ser descrita em termos de teorias desenvolvidas para redes com topologia local semelhante a grafos de tipo árvore. Em seguida, analisamos a influência de inércia na evolução das fases. Mais precisamente, generalizamos cálculos de campo-médio para osciladores de segunda-ordem acoplados em redes sem correlação de grau. Demonstramos que na presença de efeitos inerciais o parâmetro de ordem do sistema se comporta de forma histerética. Ademais, efeitos oriundos de correlações de grau são examinados. Em particular, verificamos uma interessante equivalência dinâmica entre variações nos coeficientes de assortatividade e amortecimento dos osciladores. Possíveis aplicações para situações reais são discutidas. Finalmente, abordamos o problema de duas populações de osciladores estocásticos sob a influência de acoplamentos atrativos e repulsivos. Através da aplicação da aproximação Gaussiana, derivamos um conjunto reduzido de EDOs através do qual as bifurcações do sistema foram analisadas. Além dos estados asíncrono e síncrono, verificamos a existência de padrões peculiares na dinâmica de tal sistema. Mais precisamente, observamos a formação de estados caracterizados pelo surgimento de dois aglomerados de osciladores. Caso a defasagem entre estes grupos é inferior a π, um novo ritmo de oscilação diferente da frequência natural dos vértices emerge. Comportamentos dinâmicos similares são observados em osciladores caóticos sujeitos a acoplamentos análogos.
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Lenormand, Maxime. "Initialize and Calibrate a Dynamic Stochastic Microsimulation Model: Application to the SimVillages Model." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00764929.
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Le but de cette thèse est de développer des outils statistiques permettant d'initialiser et de calibrer les modèles de microsimulation dynamique stochastique, en partant de l'exemple du modèle SimVillages (développé dans le cadre du projet Européen PRIMA). Ce modèle couple des dynamiques démographiques et économiques appliquées à une population de municipalités rurales. Chaque individu de la population, représenté explicitement dans un ménage au sein d'une commune, travaille éventuellement dans une autre, et possède sa propre trajectoire de vie. Ainsi, le modèle inclut-il des dynamiques de choix de vie, d'étude, de carrière, d'union, de naissance, de divorce, de migration et de décès. Nous avons développé, implémenté et testé les modèles et méthodes suivants: * un modèle permettant de générer une population synthétique à partir de données agrégées, où chaque individu est membre d'un ménage, vit dans une commune et possède un statut au regard de l'emploi. Cette population synthétique est l'état initial du modèle. * un modèle permettant de simuler une table d'origine-destination des déplacements domicile-travail à partir de données agrégées. * un modèle permettant d'estimer le nombre d'emplois dans les services de proximité dans une commune donnée en fonction de son nombre d'habitants et de son voisinage en termes de service. * une méthode de calibration des paramètres inconnus du modèle SimVillages de manière à satisfaire un ensemble de critères d'erreurs définis sur des sources de données hétérogènes. Cette méthode est fondée sur un nouvel algorithme d'échantillonnage séquentiel de type Approximate Bayesian Computation.
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Guan, Jinyan. "Bayesian Generative Modeling of Complex Dynamical Systems." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612950.
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This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.
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Schmeltzer, Christian. "Dynamical properties of neuronal systems with complex network structure." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17470.
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In welcher Weise hängt die Dynamik eines neuronalen Systems von den Eigenschaften seiner Netzwerkstruktur ab? Diese wichtige Fragestellung der Neurowissenschaft untersuchen wir in dieser Dissertation anhand einer analytischen und numerischen Modellierung der Aktivität großer neuronaler Netzwerke mit komplexer Struktur. Im Fokus steht die Relevanz zweier bestimmter Merkmale für die Dynamik: strukturelle Heterogenität und Gradkorrelationen. Ein zentraler Bestandteil der Dissertation ist die Entwicklung einer Molekularfeldnäherung, mit der die mittlere Aktivität heterogener, gradkorrelierter neuronaler Netzwerke berechnet werden kann, ohne dass einzelne Neuronen explizit simuliert werden müssen. Die Netzwerkstruktur wird von einer reduzierten Matrix erfasst, welche die Verbindungsstärke zwischen den Neuronengruppen beschreibt. Für einige generische Zufallsnetzwerke kann diese Matrix analytisch berechnet werden, was eine effiziente Analyse der Dynamik dieser Systeme erlaubt. Mit der Molekularfeldnäherung und numerischen Simulationen zeigen wir, dass assortative Gradkorrelationen einem neuronalen System ermöglichen, seine Aktivität bei geringer externer Anregung aufrecht zu erhalten und somit besonders sensitiv auf schwache Stimuli zu reagieren.An important question in neuroscience is how the structure and dynamics of a neuronal network relate to each other. We approach this problem by modeling the spiking activity of large-scale neuronal networks that exhibit several complex network properties. Our main focus lies on the relevance of two particular attributes for the dynamics, namely structural heterogeneity and degree correlations. As a central result, we introduce a novel mean-field method that makes it possible to calculate the average activity of heterogeneous, degree-correlated neuronal networks without having to simulate each neuron explicitly. We find that the connectivity structure is sufficiently captured by a reduced matrix that contains only the coupling between the populations. With the mean-field method and numerical simulations we demonstrate that assortative degree correlations enhance the network’s ability to sustain activity for low external excitation, thus making it more sensitive to small input signals.
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Colombini, Giulio. "Synchronisation phenomena in complex neuronal networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23904/.
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The phenomenon of neural synchronisation, a simultaneous and repeated firing of clusters of neurons, underlies many physiological functions and pathological manifestations in the brain of humans and animals, ranging from information encoding to epileptic seizures. Neural synchronisation, as a general phenomenon, can be approached theoretically in the framework of Dynamical Systems on Networks. In the present work, we do so by considering complex networks of FitzHugh-Nagumo model neurons. In the first part we consider the most understood models where each neuron treats its presynaptic neurons all on an equal footing, normalising signals with its in-degree. We study the stability of the synchronous state by devising an algorithm that destabilises it by selecting and removing links from the network, so to obtain a bipartite network. The selection is performed using a perturbative expression, which can be regarded as a specialisation of a previously introduced Spectral Centrality measure. The algorithm is tested on Erdős-Renyi, Watts-Strogatz and Barabási-Albert networks, and its behaviour is assessed from a dynamical and from a structural point of view. In the second part we consider the less studied case in which each neuron divides equally its output among the postsynaptic neurons, so to reproduce schematically the situation where a fixed quantity of neurotransmitter is subdivided between several efferent neurons. In this context a self-consistent approach is formulated and its limitations are explored. In order to extend its application to larger networks, a Mean Field Approximation is presented. The predictivity of the Mean Field Approach is then tested on the different random network models, and the results are discussed in terms of the original network properties.
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Chinellato, David Dobrigkeit 1983. "Processos dinâmicos em redes complexas." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/278371.
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Orientador: Marcus Aloizio Martinez de AguiarDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Fisica Gleb Wataghin
Made available in DSpace on 2018-08-10T18:23:05Z (GMT). No. of bitstreams: 1 Chinellato_DavidDobrigkeit_M.pdf: 15300810 bytes, checksum: 36fdea424f1c7f83a5f50742e82465f8 (MD5) Previous issue date: 2007
Resumo: Nesta tese, estudamos as propriedades estatísticas de processos dinâmicos de influência em redes complexas sujeitas a perturbações externas. Consideramos redes cujos nós admitem dois estados internos, digamos 0 e 1. Os estados internos se alteram de acordo com os estados dos nós vizinhos. Supomos que há N1 nós com estado interno fixo em 1, N0 elementos com estado interno fixo em 0 e outros N elementos com estado interno livre. Os nós com estado interno ½xo podem ser interpretados como perturbações externas à subrede de N elementos livres. Este sistema é uma generalização do modelo do eleitor [25] e pode descrever diversas situações interessantes, indo de sistemas sociais [26] para a física e a genética. Neste trabalho, calcularemos analiticamente a evolução de um sistema de rede totalmente conectada, obtendo expressões para as distribuições de equilíbrio de uma rede qualquer e também de todas as probabilidades de transição. Em seguida, generalizamos os resultados para o caso em que N0 e N1 são menores do que 1, representando um acoplamento fraco do sistema com um reservatório externo. Mostramos que os resultados exatos são excelentes aproximações para várias outras redes, incluindo redes aleatórias, reticuladas, livres de escala, estrela e mundo pequeno, e estudamos a dinâmica destas outras redes numericamente. Finalmente, demonstramos que, se os dois parâmetros da solução para redes totalmente conectadas, N0 e N1, forem alterados para valores efetivos para cada tipo de rede específico, o nosso resultado analítico explica satisfatoriamente todas as dinâmicas e estados assintóticos de outras topologias. O nosso modelo é portanto bastante geral, se aplicado cuidadosamente
Abstract: We study the statistical properties of in²uence networks subjected to external perturbations. We consider networks whose nodes have internal states that can assume the values 0 or 1. The internal states can change depending on the state of the neighboring nodes. We let N1 nodes be frozen in the state 1, N0 be frozen in the state 0 and the remaining N nodes be free to change their internal state. The frozen nodes are interpreted as external perturbations to the sub-network of N free nodes. The system is a generalization of the voter model [25] and can describe a variety of interesting situations, from social systems [26] to physics and genetics. In this thesis, we calculate analytically the equilibrium distribution and the transition probabilities between any two states for arbitrary values of N, N1 and N0 for the case of fully connected networks. Next we generalize the results for the case where N0 and N1 are smaller than 1, representing the weak coupling of the network to an external reservoir. We show that our exact results are excellent approximations for several other topologies, including random, regular lattices, scale-free, star and small world networks, and study the dynamics of these other networks numerically. We then proceed to show that, by appropriately tuning the two parameters from the solution from fully connected networks, N0and N1, to eÿective values when dealing with other, more sophisticated network types, we can easily explain their asymptotic network behaviour. Our model is therefore quite general in applicability, if used consciously
Mestrado
Física Estatistica e Termodinamica
Mestre em Física
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Chetty, Vasu Nephi. "Theory and Applications of Network Structure of Complex Dynamical Systems." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6270.
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One of the most powerful properties of mathematical systems theory is the fact that interconnecting systems yields composites that are themselves systems. This property allows for the engineering of complex systems by aggregating simpler systems into intricate patterns. We call these interconnection patterns the "structure" of the system. Similarly, this property also enables the understanding of complex systems by decomposing them into simpler parts. We likewise call the relationship between these parts the "structure" of the system. At first glance, these may appear to represent identical views of structure of a system. However, further investigation invites the question: are these two notions of structure of a system the same? This dissertation answers this question by developing a theory of dynamical structure. The work begins be distinguishing notions of structure from their associated mathematical representations, or models, of a system. Focusing on linear time invariant (LTI) systems, the key technical contributions begin by extending the definition of the dynamical structure function to all LTI systems and proving essential invariance properties as well as extending necessary and sufficient conditions for the reconstruction of the dynamical structure function from data. Given these extensions, we then develop a framework for analyzing the structures associated with different representations of the same system and use this framework to show that interconnection (or subsystem) structures are not necessarily the same as decomposition (or signal) structures. We also show necessary and sufficient conditions for the reconstruction of the interconnection (or subsystem) structure for a class of systems. In addition to theoretical contributions, this work also makes key contributions to specific applications. In particular, network reconstruction algorithms are developed that extend the applicability of existing methods to general LTI systems while improving the computational complexity. Also, a passive reconstruction method was developed that enables reconstruction without actively probing the system. Finally, the structural theory developed here is used to analyze the vulnerability of a system to simultaneous attacks (coordinated or uncoordinated), enabling a novel approach to the security of cyber-physical-human systems.
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Yang, Ang Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "A networked multi-agent combat model : emergence explained." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2007. http://handle.unsw.edu.au/1959.4/38823.
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Simulation has been used to model combat for a long time. Recently, it has been accepted that combat is a complex adaptive system (CAS). Multi-agent systems (MAS) are also considered as a powerful modelling and development environment to simulate combat. Agent-based distillations (ABD) - proposed by the US Marine Corp - are a type of MAS used mainly by the military for exploring large scenario spaces. ABDs that facilitated the analysis and understanding of combat include: ISAAC, EINSTein, MANA, CROCADILE and BactoWars. With new concepts such as networked forces, previous ABDs can implicitly simulate a networked force. However, the architectures of these systems limit the potential advantages gained from the use of networks. In this thesis, a novel network centric multi-agent architecture (NCMAA) is pro-posed, based purely on network theory and CAS. In NCMAA, each relationship and interaction is modelled as a network, with the entities or agents as the nodes. NCMAA offers the following advantages: 1. An explicit model of interactions/relationships: it facilitates the analysis of the role of interactions/relationships in simulations; 2. A mechanism to capture the interaction or influence between networks; 3. A formal real-time reasoning framework at the network level in ABDs: it interprets the emergent behaviours online. For a long time, it has been believed that it is hard in CAS to reason about emerging phenomena. In this thesis, I show that despite being almost impossible to reason about the behaviour of the system by looking at the components alone because of high nonlinearity, it is possible to reason about emerging phenomena by looking at the network level. This is undertaken through analysing network dynamics, where I provide an English-like reasoning log to explain the simulation. Two implementations of a new land-combat system called the Warfare Intelligent System for Dynamic Optimization of Missions (WISDOM) are presented. WISDOM-I is built based on the same principles as those in existing ABDs while WISDOM-II is built based on NCMAA. The unique features of WISDOM-II include: 1. A real-time network analysis toolbox: it captures patterns while interaction is evolving during the simulation; 2. Flexible C3 (command, control and communication) models; I 3. Integration of tactics with strategies: the tactical decisions are guided by the strategic planning; 4. A model of recovery: it allows users to study the role of recovery capability and resources; 5. Real-time visualization of all possible information: it allows users to intervene during the simulation to steer it differently in human-in-the-loop simulations. A comparison between the fitness landscapes of WISDOM-I and II reveals similarities and differences, which emphasise the importance and role of the networked architecture and the addition of strategic planning. Lastly but not least, WISDOM-II is used in an experiment with two setups, with and without strategic planning in different urban terrains. When the strategic planning was removed, conclusions were similar to traditional ABDs but were very different when the system ran with strategic planning. As such, I show that results obtained from traditional ABDs - where rational group planning is not considered - can be misleading. Finally, the thesis tests and demonstrates the role of communication in urban ter-rains. As future warfighting concepts tend to focus on asymmetric warfare in urban environments, it was vital to test the role of networked forces in these environments. I demonstrate that there is a phase transition in a number of situations where highly dense urban terrains may lead to similar outcomes as open terrains, while medium to light dense urban terrains have different dynamics
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Martinet, Lucie. "Réseaux dynamiques de terrain : caractérisation et propriétés de diffusion en milieu hospitalier." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1010/document.
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Durant cette thèse, nous nous sommes intéressés aux outils permettant d'extraire les propriétés structurelles et temporelles de réseaux dynamiques ainsi que les caractéristiques de certains scénarios de diffusion pouvant s'opérer sur ces réseaux. Nous avons travaillé sur un jeu de données spécifiques, issu du projet MOSAR, qui comporte entre autre le réseau de proximité des personnes au cours du temps durant 6 mois à l'hôpital de Berk-sur-mer. Ce réseau est particulier dans le sens où il est constitué de trois dimensions: temporelle, structurelle par la répartition des personnes en services et fonctionnelle car chaque personne appartient à une catégorie socio-professionnelle. Pour chacune des dimensions, nous avons utilisé des outils existants en physique statistique ainsi qu'en théorie des graphes pour extraire des informations permettant de décrire certaines propriétés du réseau. Cela nous a permis de souligner le caractère très structuré de la répartition des contacts qui suit la répartition en services et mis en évidence les accointances entre certaines catégories professionnelles. Concernant la partie temporelle, nous avons mis en avant l'évolution périodique circadienne et hebdomadaire ainsi que les différences fondamentales entre l'évolution des interactions des patients et celle des personnels. Nous avons aussi présenté des outils permettant de comparer l'activité entre deux périodes données et de quantifier la similarité de ces périodes. Nous avons ensuite utilisé la technique de simulation pour extraire des propriétés de diffusion de ce réseau afin de donner quelques indices pour établir une politique de préventionIn this thesis, we focus on tools whose aim is to extract structural and temporal properties of dynamic networks as well as diffusion characteristics which can occur on these networks. We work on specific data, from the European MOSAR project, including the network of individuals proximity from time to time during 6 months at the Brek-sur-Mer Hospital. The studied network is notable because of its three dimensions constitution : the structural one induced by the distribution of individuals into distinct services, the functional dimension due to the partition of individual into groups of socio-professional categories and the temporal dimension.For each dimension, we used tools well known from the areas of statistical physics as well as graphs theory in order to extract information which enable to describe the network properties. These methods underline the specific structure of the contacts distribution which follows the individuals distribution into services. We also highlight strong links within specific socio-professional categories. Regarding the temporal part, we extract circadian and weekly patterns and quantify the similarities of these activities. We also notice distinct behaviour within patients and staff evolution. In addition, we present tools to compare the network activity within two given periods. To finish, we use simulations techniques to extract diffusion properties of the network to find some clues in order to establish a prevention policy
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Zhou, Shu. "Exploring network models under sampling." Kansas State University, 2015. http://hdl.handle.net/2097/20349.
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Master of ScienceDepartment of Statistics
Perla Reyes
Networks are defined as sets of items and their connections. Interconnected items are represented by mathematical abstractions called vertices (or nodes), and the links connecting pairs of vertices are known as edges. Networks are easily seen in everyday life: a network of friends, the Internet, metabolic or citation networks. The increase of available data and the need to analyze network have resulted in the proliferation of models for networks. However, for networks with billions of nodes and edges, computation and inference might not be achieved within a reasonable amount of time or budget. A sampling approach seems a natural choice, but traditional models assume that we can have access to the entire network. Moreover, when data is only available for a sampled sub-network conclusions tend to be extrapolated to the whole network/population without regard to sampling error. The statistical problem this report addresses is the issue of how to sample a sub-network and then draw conclusions about the whole network. Are some sampling techniques better than others? Are there more efficient ways to estimate parameters of interest? In which way can we measure how effectively my method is reproducing the original network? We explore these questions with a simulation study on Mesa High School students' friendship network. First, to assess the characteristics of the whole network, we applied the traditional exponential random graph model (ERGM) and a stochastic blockmodel to the complete population of 205 students. Then, we drew simple random and stratified samples of 41 students, applied the traditional ERGM and the stochastic blockmodel again, and defined a way to generalized the sample findings to the population friendship network of 205 students. Finally, we used the degree distribution and other network statistics to compare the true friendship network with the projected one. We achieved the following important results: 1) as expected stratified sampling outperforms simple random sampling when selecting nodes; 2) ERGM without restrictions offers a poor estimate for most of the tested parameters; and 3) the Bayesian stochastic blockmodel estimation using a strati ed sample of nodes achieves the best results.
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Cantin, Guillaume. "Étude de réseaux complexes de systèmes dynamiques dissipatifs ou conservatifs en dimension finie ou infinie. Application à l'analyse des comportements humains en situation de catastrophe." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMLH16/document.
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Cette thèse est consacrée à l'étude de la dynamique des systèmes complexes. Nous construisons des réseaux couplés à partir de multiples instances de systèmes dynamiques déterministes, donnés par des équations différentielles ordinaires ou des équations aux dérivées partielles de type parabolique, qui décrivent un problème d'évolution. Nous étudions le lien entre la dynamique interne à chaque nœud du réseau, les éléments de la topologie du graphe portant ce réseau, et sa dynamique globale. Nous recherchons les conditions de couplage qui favorisent une dynamique globale particulière à l'échelle du réseau, et étudions l'impact des interactions sur les bifurcations identifiées sur chaque nœud. Nous considérons en particulier des réseaux couplés de systèmes de réaction-diffusion, dont nous étudions le comportement asymptotique, en recherchant des régions positivement invariantes, et en démontrant l'existence d'attracteurs exponentiels de dimension fractale finie, à partir d'estimations d'énergie qui révèlent la nature dissipative de ces réseaux de systèmes de réaction-diffusion. Ces questions sont étudiées dans le cadre de quelques applications. En particulier, nous considérons un modèle mathématique pour l'étude géographique des réactions comportementales d'individus, au sein d'une population en situation de catastrophe. Nous présentons les éléments de modélisation associés, ainsi que son étude mathématique, avec une analyse de la stabilité des équilibres et de leurs bifurcations. Nous établissons l'importance capitale des chemins d'évacuation dans les réseaux complexes construits à partir de ce modèle, pour atteindre l'équilibre attendu de retour au comportement du quotidien pour l'ensemble de la population considérée, tout en évitant une propagation du comportement de panique. D'autre part, la recherche de solutions périodiques émergentes dans les réseaux d'oscillateurs nous amène à considérer des réseaux complexes de systèmes hamiltoniens pour lesquels nous construisons des perturbations polynomiales qui provoquent l'apparition de cycles limites, problématique liée au XVIème problème de HilbertThis thesis is devoted to the study of the dynamics of complex systems. We consider coupled networks built with multiple instances of deterministicdynamical systems, defined by ordinary differential equations or partial differential equations of parabolic type, which describe an evolution problem.We study the link between the internal dynamics of each node in the network, its topology, and its global dynamics. We analyze the coupling conditions which favor a particular dynamics at the network's scale, and study the impact of the interactions on the bifurcations identified on each node. In particular, we consider coupled networks of reaction-diffusion systems; we analyze their asymptotic behavior by searching positively invariant regions, and proving the existence of exponential attractors of finite fractal dimension, derived from energy estimates which suggest the dissipative nature of those networks of reaction-diffusion systems.Our framework includes the study of multiple applications. Among them, we consider a mathematical model for the geographical analysis of behavioral reactions of individuals facing a catastrophic event. We present the modeling choices that led to the study of this evolution problem, and its mathematical study, with a stability and bifurcation analysis of the equilibria. We highlight the decisive role of evacuation paths in coupled networks built from this model, in order to reach the expected equilibrium corresponding to a global return of all individuals to the daily behavior, avoiding a propagation of panic. Furthermore, the research of emergent periodic solutions in complex networks of oscillators brings us to consider coupled networks of hamiltonian systems, for which we construct polynomial perturbationswhich provoke the emergence of limit cycles, question which is related to the sixteenth Hilbert's problem
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Dalle, Pezze Piero. "Dynamical models of the mammalian target of rapamycin network in ageing." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2183.
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The mammalian Target of Rapamycin (mTOR)kinase is a central regulator of cellular growth and metabolism and plays an important role in ageing and age- related diseases. The increase of invitro data collected to extend our knowledge on its regulation, and consequently improve drug intervention,has highlighted the complexity of the mTOR network. This complexity is also aggravated by the intrinsic time-dependent nature of cellular regulatory network cross-talks and feedbacks. Systems biology constitutes a powerful tool for mathematically for- malising biological networks and investigating such dynamical properties. The present work discusses the development of three dynamical models of the mTOR network. The first aimed at the analysis of the current literature-based hypotheses of mTOR Complex2(mTORC2)regulation. For each hypothesis, the model predicted specific differential dynamics which were systematically tested by invitro experiments. Surprisingly, nocurrent hypothesis could explain the data and a new hypothesis of mTORC2 activation was proposed. The second model extended the previous one with an AMPK module. In this study AMPK was reported to be activated by insulin. Using a hypothesis ranking approach based on model goodness-of-fit, AMPK activity was insilico predicted and in vitro tested to be activated by the insulin receptor substrate(IRS).Finally,the last model linked mTOR with the oxidative stress response, mitochondrial reg- ulation, DNA damage and FoxO transcription factors. This work provided the characterisation of a dynamical mechanism to explain the state transition from normal to senescent cells and their reversibility of the senescentphenotype.
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Tupikina, Liubov. "Temporal and spatial aspects of correlation networks and dynamical network models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17746.
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In der vorliegenden Arbeit untersuchte ich die komplexen Strukturen von Netzwerken, deren zeitliche Entwicklung, die Interpretationen von verschieden Netzwerk-Massen und die Klassen der Prozesse darauf. Als Erstes leitete ich Masse für die Charakterisierung der zeitlichen Entwicklung der Netzwerke her, um räumlich Veränderungsmuster zu erkennen. Als Nächstes führe ich eine neue Methode zur Konstruktion komplexer Netzwerke von Flussfeldern ein, bei welcher man das Set-up auch rein unter Berufung Berufung auf das Geschwindigkeitsfeld ändern kann. Diese Verfahren wurden für die Korrelationen skalarer Grössen, z. B. Temperatur, entwickelt, welche eine Advektions-Diffusions-Dynamik in der Gegenwart von Zwingen und Dissipation. Die Flussnetzwerk-Methode zur Zeitreihenanalyse konstruiert die Korrelationsmatrizen und komplexen Netzwerke. Dies ermöglicht die Charakterisierung von Transport in Flüssigkeiten, die Identifikation verschiedene Misch-Regimes in dem Fluss und die Anwendung auf die Advektions-DiffusionsDynamik, Klimadaten und anderen Systemen, in denen Teilchentransport eine entscheidende Rolle spielen. Als Letztes, entwickelte ich ein neuartiges Heterogener Opinion Status Modell (HOpS) und Analysetechnik basiert auf Random Walks und Netzwerktopologie Theorien, um dynamischen Prozesse in Netzwerken zu studieren, wie die Verbreitung von Meinungen in sozialen Netzwerken oder Krankheiten in der Gesellschaft. Ein neues Modell heterogener Verbreitung auf einem Netzwerk wird als Beispielssystem für HOpS verwendent, um die vergleichsweise Einfachheit zu nutzen. Die Analyse eines diskreten Phasenraums des HOPS-Modells hat überraschende Eigenschaften, welches sensibel auf die Netzwerktopologie reagieren. Sie können verallgemeinert werden, um verschiedene Klassen von komplexen Netzwerken zu quantifizieren, Transportphänomene zu charakterisieren und verschiedene Zeitreihen zu analysieren.In the thesis I studied the complex architectures of networks, the network evolution in time, the interpretation of the networks measures and a particular class of processes taking place on complex networks. Firstly, I derived the measures to characterize temporal networks evolution in order to detect spatial variability patterns in evolving systems. Secondly, I introduced a novel flow-network method to construct networks from flows, that also allows to modify the set-up from purely relying on the velocity field. The flow-network method is developed for correlations of a scalar quantity (temperature, for example), which satisfies advection-diffusion dynamics in the presence of forcing and dissipation. This allows to characterize transport in the fluids, to identify various mixing regimes in the flow and to apply this method to advection-diffusion dynamics, data from climate and other systems, where particles transport plays a crucial role. Thirdly, I developed a novel Heterogeneous Opinion-Status model (HOpS) and analytical technique to study dynamical processes on networks. All in all, methods, derived in the thesis, allow to quantify evolution of various classes of complex systems, to get insight into physical meaning of correlation networks and analytically to analyze processes, taking place on networks.
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Grau, Leguia Marc. "Automatic reconstruction of complex dynamical networks." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666631.
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Un problema principal de la ciència de xarxes és com reconstruir (inferir) la topologia d’una xarxa real a partir de senyals mesurades de les seves unitats internes. Entendre la arquitectura d’una xarxa complexa és clau, no només per comprendre el seu funcionament, sinó també per predir i controlar el seu comportament. Els mètodes actualment disponibles es centren principalment en la detecció d’enllaços de xarxes no direccio- nals i sovint requereixen suposicions fortes sobre el sistema. Tanmateix, molts d’aquests mètodes no es poden aplicar a xarxes amb connexions direccionals. Per abordar aquest problema, en aquesta tesis ens centrarem en la inferència de xarxes direccionals. Concretament, desenvolupem un mètode de reconstrucció de xarxes basat en models que combina estadístiques de correlacions de derivades amb recuit simulat. A més, desenvolupem un mètode de reconstrucció basat en dades cimentat en una mesura d’interpedendència no lineal. Aquest mètode permet inferir la topologia de xarxes direccionals d’oscil.ladors caòtics de Lorenz per un subordre de la força d’acoblament i la densitat de la xarxa. Finalment, apliquem el mètode basat en dades a gravacions electroencefalogràfiques d’un pacient amb epilèpsia. Les xarxes cerebrals funcionals obtingu- des a partir d’aquest mètode són coherents amb la informació mèdica disponible.Un problema principal de la ciencia de redes es cómo reconstruir (inferir) la topología de una red real usando la señales medidas de sus unidades internas. Entender la arquitectura de redes complejas es clave, no solo para entender su funcionamiento pero también para predecir y controlar su comportamiento. Los métodos existentes se focalizan en la detección de redes no direccionales y normalmente requieren fuertes suposicio- nes sobre el sistema. Sin embargo, muchos de estos métodos no pueden ser aplicados en redes con conexiones direccionales. Para abordar este problema, en esta tesis estudiamos la reconstrucción de redes direccio- nales. En concreto, desarrollamos un método de reconstrucción basado en modelos que combina estadísticas de correlaciones de derivadas con recocido simulado. Además, desarrollamos un método basado en datos cimentado en una medida d’interdependencia no lineal. Este método permite inferir la topología de redes direccionales de osciladores caóticos de Lorenz para un subrango de la fuerza de acoplamiento y densidad de la red. Finalmente, aplicamos el método basado en datos a grabaciones electroencefalográficas de un paciente con epilepsia. Las redes cerebra- les funcionales obtenidas usando este método son consistentes con la información médica disponible.
A foremost problem in network science is how to reconstruct (infer) the topology of a real network from signals measured from its internal units. Grasping the architecture of complex networks is key, not only to understand their functioning, but also to predict and control their behaviour. Currently available methods largely focus on the detection of links of undirected networks and often require strong assumptions about the system. However, many of these methods cannot be applied to networks with directional connections. To address this problem, in this doctoral work we focus at the inference of directed networks. Specifically, we develop a model-based network reconstruction method that combines statistics of derivative-variable correlations with simulated annealing. We furthermore develop a data-driven reconstruction method based on a nonlinear interdependence measure. This method allows one to infer the topology of directed networks of chaotic Lorenz oscillators for a subrange of the coupling strength and link density. Finally, we apply the data-driven method to multichannel electroencephalographic recordings from an epilepsy patient. The functional brain networks obtained from this approach are consistent with the available medical information.
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Maloo, Akshay. "Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25296.
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Dynamic Behavior Visualizer (DBV) is a visual analytics environment to visualize the spatial and temporal movements and behavioral changes of an individual or a group, e.g. family within a realistic urban environment. DBV is specifically designed to visualize the adaptive behavioral changes, as they pertain to the interactions with multiple inter-dependent infrastructures, in the aftermath of a large crisis, e.g. hurricane or the detonation of an improvised nuclear device. DBV is web-enabled and thus is easily accessible to any user with access to a web browser. A novel aspect of the system is its scale and fidelity. The goal of DBV is to synthesize information and derive insight from it; detect the expected and discover the unexpected; provide timely and easily understandable assessment and the ability to piece together all this information.Master of Science
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Hui, Zi. "Spatial structure of complex network and diffusion dynamics." Thesis, Le Mans, 2013. http://www.theses.fr/2013LEMA1005/document.
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Dans le développement récent des sciences de réseau, réseaux contraints spatiales sont devenues un objet d'une enquête approfondie. Spatiales des réseaux de contraintes sont intégrées dans l'espace de configuration. Leurs structures et les dynamiques sont influencées par la distance spatiale. Ceci est prouvé par les données empiriques de plus en plus sur des systèmes réels montrant des lois exponentielles ou de distribution d'énergie distance spatiale de liens. Dans cette thèse, nous nous concentrons sur la structure de réseau spatial avec une distribution en loi de puissance spatiale. Plusieurs mécanismes de formation de la structure et de la dynamique de diffusion sur ces réseaux sont pris en considération. D'abord, nous proposons un réseau évolutif construit en l'espace de configuration d'un mécanisme de concurrence entre le degré et les préférences de distance spatiale. Ce mécanisme est décrit par un a^'fc- + (1 — a)^'lL_,1, où ki est le degré du noeud i et rni est la distance spatiale entre les noeuds n et i. En réglant le paramètre a, le réseau peut être fait pour changer en continu à partir du réseau spatiale entraînée (a = 0) pour le réseau sans échelle (a = 1). La structure topologique de notre modèle est comparé aux données empiriques de réseau de courrier électronique avec un bon accord. Sur cette base, nous nous concentrons sur la dynamique de diffusion sur le réseau axé sur spatiale (a — 0). Le premier modèle, nous avons utilisé est fréquemment employée dans l'étude de la propagation de l'épidémie: ['spatiale susceptible-infecté-susceptible (SIS) modèle. Ici, le taux de propagation entre deux noeuds connectés est inversement proportionnelle à leur distance spatiale. Le résultat montre que la diffusion efficace de temps augmente avec l'augmentation de a. L'existence d'seuil épidémique générique est observée, dont la valeur dépend du paramètre a Le seuil épidémique maximum et le ratio minimum fixe de noeuds infectés localiser simultanément dans le intervalle 1.5 < a < 2.Puisque le réseau spatiale axée a bien défini la distance spatiale, ce modèle offre une occasion d'étudier la dynamique de diffusion en utilisant les techniques habituelles de la mécanique statistique. Tout d'abord, compte tenu du fait que la diffusion est anormale en général en raison de l'importante long plage de propagation, nous introduisons un coefficient de diffusion composite qui est la somme de la diffusion d'habitude constante D des lois de la Fick appliqué sur différentes distances de transfert possibles sur le réseau. Comme prévu, ce coefficient composite diminue avec l'augmentation de a. et est une bonne mesure de l'efficacité de la diffusion. Notre seconde approche pour cette diffusion anormale est de calculer le déplacement quadratique moyen (l²) à identifier une constante de diffusion D' et le degré de la anomalousness y avec l'aide de la loi de puissance (l²) = 4D'ty. D' comportements de la même manière que D, i.e.. elle diminue avec l'augmentation de a. y est inférieur à l'unité (subdiffusion) et tend à un (diffusion normale) que a augmenteIn the recent development of network sciences, spatial constrained networks have become an object of extensive investigation. Spatial constrained networks are embedded in configuration space. Their structures and dynamics are influenced by spatial distance. This is proved by more and more empirical data on real Systems showing exponential or power laws spatial distance distribution of links. In this dissertation, we focus on the structure of spatial network with power law spatial distribution. Several mechanisms of structure formation and diffusion dynamics on these networks are considered. First we propose an evolutionary network constructed in the configuration space with a competing mechanism between the degree and the spatial distance preferences. This mechanism is described by a ki + (1 — a), where ki is the degree of node i and rni is the spatial distance between nodes n and i. By adjusting parameter a, the network can be made to change continuously from the spatial driven network (a = 0) to the scale-free network (a = 1). The topological structure of our model is compared to the empirical data from email network with good agreement. On this basis, we focus on the diffusion dynamics on spatial driven network (a = 0). The first model we used is frequently employed in the study of epidemie spreading : the spatial susceptible-infected-susceptible (SIS) model. Here the spreading rate between two connected nodes is inversely proportional to their spatial distance. The result shows that the effective spreading time increases with increasing a. The existence of generic epidemic threshold is observed, whose value dépends on parameter a. The maximum épidemic threshold and the minimum stationary ratio of infected nodes simultaneously locate in the interval 1.5 < a < 2. Since the spatial driven network has well defined spatial distance, this model offers an occasion to study the diffusion dynamics by using the usual techniques of statistical mechanics. First, considering the fact that the diffusion is anomalous in general due to the important long-range spreading, we introduce a composite diffusion coefficient which is the sum of the usual diffusion constant D of the Fick's laws applied over different possible transfer distances on the network. As expected, this composite coefficient decreases with increasing a and is a good measure of the efficiency of the diffusion. Our second approach to this anomalous diffusion is to calculate the mean square displacement (l²) to identify a diffusion constant D' and the degree of thé anomalousness y with the help of the power law {l²} = 4D'ty. D' behaviors in the same way as D, i.e., it decreases with increasing a. y is smaller than unity (subdiffusion) and tends to one (normal diffusion) as a increases
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Baek, Seong Cheol. "Dynamical Analysis and Decentralized Control of Power Packet Network." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263664.
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MacKenzie, Tony. "Create accurate numerical models of complex spatio-temporal dynamical systems with holistic discretisation." University of Southern Queensland, Faculty of Sciences, 2005. http://eprints.usq.edu.au/archive/00001466/.
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This dissertation focuses on the further development of creating accurate numerical models of complex dynamical systems using the holistic discretisation technique [Roberts, Appl. Num. Model., 37:371-396, 2001]. I extend the application from second to fourth order systems and from only one spatial dimension in all previous work to two dimensions (2D). We see that the holistic technique provides useful and accurate numerical discretisations on coarse grids. We explore techniques to model the evolution of spatial patterns governed by pdes such as the Kuramoto-Sivashinsky equation and the real-valued Ginzburg-Landau equation. We aim towards the simulation of fluid flow and convection in three spatial dimensions. I show that significant steps have been taken in this dissertation towards achieving this aim. Holistic discretisation is based upon centre manifold theory [Carr, Applications of centre manifold theory, 1981] so we are assured that the numerical discretisation accurately models the dynamical system and may be constructed systematically. To apply centre manifold theory the domain is divided into elements and using a homotopy in the coupling parameter, subgrid scale fields are constructed consisting of actual solutions of the governing partial differential equation(pde). These subgrid scale fields interact through the introduction of artificial internal boundary conditions. View the centre manifold (macroscale) as the union of all states of the collection of subgrid fields (microscale) over the physical domain. Here we explore how to extend holistic discretisation to the fourth order Kuramoto-Sivashinsky pde. I show that the holistic models give impressive accuracy for reproducing the steady states and time dependent phenomena of the Kuramoto-Sivashinsky equation on coarse grids. The holistic method based on local dynamics compares favourably to the global methods of approximate inertial manifolds. The excellent performance of the holistic models shown here is strong evidence in support of the holistic discretisation technique. For shear dispersion in a 2D channel a one-dimensional numerical approximation is generated directly from the two-dimensional advection-diffusion dynamics. We find that a low order holistic model contains the shear dispersion term of the Taylor model [Taylor, IMA J. Appl. Math., 225:473-477, 1954]. This new approach does not require the assumption of large x scales, formerly absolutely crucial in deriving the Taylor model. I develop holistic discretisation for two spatial dimensions by applying the technique to the real-valued Ginzburg-Landau equation as a representative example of second order pdes. The techniques will apply quite generally to second order reaction-diffusion equations in 2D. This is the first study implementing holistic discretisation in more than one spatial dimension. The previous applications of holistic discretisation have developed algebraic forms of the subgrid field and its evolution. I develop an algorithm for numerical construction of the subgrid field and its evolution for 1D and 2D pdes and explore various alternatives. This new development greatly extends the class of problems that may be discretised by the holistic technique. This is a vital step for the application of the holistic technique to higher spatial dimensions and towards discretising the Navier-Stokes equations.
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Choe, Sehyo Charley. "Models of complex adaptive systems with underlying network structure." Thesis, University of Oxford, 2007. https://ora.ox.ac.uk/objects/uuid:1cb8cb96-d27f-4543-9065-0e38a4297435.
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This thesis explores the effect of different types of underlying network structure on the dynamical behaviour of a competitive population - a situation encountered in many real-world complex systems. In the first part of the thesis, I focus on generic, but abstract, multi-agent systems. I start by presenting analytic and numerical results for a population of heterogeneous, decision-making agents competing for some limited global resource, in which connections arise unintentionally between agents as a by-product of their strategy choices. I show that accounting for the resulting groups of strongly-correlated agents - in particular, the crowds and so-called 'anticrowds' - yields an accurate analytic description of the systems dynamics. I then introduce a local communication network between the agents, enabling them to intentionally share information among themselves. Such an interaction network leads to highly non-trivial dynamics, forcing a trade-off between individual and global success. Introducing corruption into the information being exchanged between agents, gives rise to a novel phase transition. I then provide a quantitative analytic theory of these various numerical results by generalizing the Crowd-Anticrowd formalism to include such local interactions. In the second part of the thesis, I consider a real-world system which also features competitive populations and networks - a cancer tumour, which contains cancerous cells competing for space and nutrients in the presence of an underlying vasculature structure. To simplify the analysis and comparison to real clinical data, the model chosen is far simpler than that discussed in the first part of the thesis - however despite its simplicity, the model is shown to yield remarkably good agreement with empirical findings. In addition, the model shows how different treatment methods can lead to a wide variety of unexpected re-growth behaviours of the tumour.
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Agostinho, Carlos Manuel Melo. "Sustainability of systems interoperability in dynamic business networks." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8582.
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Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de ComputadoresCollaborative networked environments emerged with the spread of the internet, contributing to overcome past communication barriers, and identifying interoperability as an essential property to support businesses development. When achieved seamlessly, efficiency is increased in the entire product life cycle support. However, due to the different sources of knowledge, models and semantics, enterprise organisations are experiencing difficulties exchanging critical information, even when they operate in the same business environments. To solve this issue, most of them try to attain interoperability by establishing peer-to-peer mappings with different business partners, or use neutral data and product standards as the core for information sharing, in optimized networks. In current industrial practice, the model mappings that regulate enterprise communications are only defined once, and most of them are hardcoded in the information systems. This solution has been effective and sufficient for static environments, where enterprise and product models are valid for decades. However, more and more enterprise systems are becoming dynamic, adapting and looking forward to meet further requirements; a trend that is causing new interoperability disturbances and efficiency reduction on existing partnerships. Enterprise Interoperability (EI) is a well established area of applied research, studying these problems, and proposing novel approaches and solutions. This PhD work contributes to that research considering enterprises as complex and adaptive systems, swayed to factors that are making interoperability difficult to sustain over time. The analysis of complexity as a neighbouring scientific domain, in which features of interoperability can be identified and evaluated as a benchmark for developing a new foundation of EI, is here proposed. This approach envisages at drawing concepts from complexity science to analyse dynamic enterprise networks and proposes a framework for sustaining systems interoperability, enabling different organisations to evolve at their own pace, answering the upcoming requirements but minimizing the negative impact these changes can have on their business environment.
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Passey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.
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This thesis advances the theory of network specialization by characterizing the effect of network specialization on the eigenvectors of a network. We prove and provide explicit formulas for the eigenvectors of specialized graphs based on the eigenvectors of their parent graphs. The second portion of this thesis applies network specialization to learning problems. Our work focuses on training reservoir computers to mimic the Lorentz equations. We experiment with random graph, preferential attachment and small world topologies and demonstrate that the random removal of directed edges increases predictive capability of a reservoir topology. We then create a new network model by growing networks via targeted application of the specialization model. This is accomplished iteratively by selecting top preforming nodes within the reservoir computer and specializing them. Our generated topology out-preforms all other topologies on average.
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Sousa, Fabiano Berardo de. "Análise de modelo de Hopfield com topologia de rede complexa." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30012014-111520/.
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Redes neurais biológicas contêm bilhões de células (neurônios) agrupadas em regiões espacial e funcionalmente distintas. Elas também apresentam comportamentos complexos, tais como dinâmicas periódicas e caóticas. Na área da Inteligência Artificial, pesquisas mostram que Redes Neurais Caóticas, isto é, modelos de Redes Neurais Artificiais que operam com dinâmicas complexas, são mais eficientes do que modelos tradicionais no que diz respeito a evitar memórias espúrias. Inspirado pelo fato de que o córtex cerebral contém agrupamentos de células e motivado pela eficiência no uso de dinâmicas complexas, este projeto de pesquisa investiga o comportamento dinâmico de um modelo de Rede Neural Artificial Recorrente, como o de Hopfield, porém com a topologia sináptica reorganizada a ponto de originar agrupamentos de neurônios, tal como acontece em uma Rede Complexa quando esta apresenta uma estrutura de comunidades. O modelo de treinamento tradicional de Hopfield também é alterado para uma regra de aprendizado que posta os padrões em ciclos, gerando uma matriz de pesos assimétrica. Resultados indicam que o modelo proposto oscila entre comportamentos periódicos e caóticos, dependendo do grau de fragmentação das sinapses. Com baixo grau de fragmentação, a rede opera com dinâmica periódica, como consequência da regra de treinamento utilizada. Dinâmicas caóticas parecem surgir quando existe um alto grau de fragmentação. Mostra-se, também, que é possível obter caoticidade em uma topologia adequadamente modular, ou seja, como uma estrutura de comunidades válida. Desta forma, este projeto de pesquisa provê uma metodologia alternativa para se construir um modelo de Rede Neural Artificial que realiza tarefas de reconhecimento de padrões, explorando dinâmicas complexas por meio de uma estrutura de conexões que se mostra mais similar à topologia existente no cérebroBiological neural networks contain billions of neurons divided in spatial and functional clusters to perform dierent tasks. It also operates with complex dynamics such as periodic and chaotic ones. It has been shown that Chaotic Neural Networks are more efficient than conventional recurrent neural networks in avoiding spurious memory. Inspired by the fact that the cerebral cortex has speficic groups of cells and motivated by the efficiency of complex behaviors, in this document we investigate the dynamics of a recurrent neural network, as the Hopfield one, but with neurons coupled in such a way to form a complex network community structure. Also, we generate an asymmetric weight matrix placing pattern cycles during learning. Our study shows that the network can operate with periodic and chaotic dynamics, depending on the degree of the connection\'s fragmentation. For low fragmentation degree, the network operates with periodic dynamic duo to the employed learning rule. Chaotic behavior seems to rise for a high fragmentation degree. We also show that the neural network can hold both chaotic dynamic and a high value of modularity measure at the same time, indicating an acceptable community structure. These findings provide an alternative way to design dynamical neural networks to perform pattern recognition tasks exploiting periodic and chaotic dynamics by using a more similar topology to the topology of the brain
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Viamontes, Esquivel Alcides. "Narrowing the gap between network models and real complex systems." Doctoral thesis, Umeå universitet, Institutionen för fysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-89149.
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Simple network models that focus only on graph topology or, at best, basic interactions are often insufficient to capture all the aspects of a dynamic complex system. In this thesis, I explore those limitations, and some concrete methods of resolving them. I argue that, in order to succeed at interpreting and influencing complex systems, we need to take into account slightly more complex parts, interactions and information flows in our models.This thesis supports that affirmation with five actual examples of applied research. Each study case takes a closer look at the dynamic of the studied problem and complements the network model with techniques from information theory, machine learning, discrete maths and/or ergodic theory. By using these techniques to study the concrete dynamics of each system, we could obtain interesting new information. Concretely, we could get better models of network walks that are used on everyday applications like journal ranking. We could also uncover asymptotic characteristics of an agent-based information propagation model which we think is the basis for things like belief propaga-tion or technology adoption on society. And finally, we could spot associations between antibiotic resistance genes in bacterial populations, a problem which is becoming more serious every day.
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Ferrat, L. "Machine learning and statistical analysis of complex mathematical models : an application to epilepsy." Thesis, University of Exeter, 2019. http://hdl.handle.net/10871/36090.
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The electroencephalogram (EEG) is a commonly used tool for studying the emergent electrical rhythms of the brain. It has wide utility in psychology, as well as bringing a useful diagnostic aid for neurological conditions such as epilepsy. It is of growing importance to better understand the emergence of these electrical rhythms and, in the case of diagnosis of neurological conditions, to find mechanistic differences between healthy individuals and those with a disease. Mathematical models are an important tool that offer the potential to reveal these otherwise hidden mechanisms. In particular Neural Mass Models (NMMs), which describe the macroscopic activity of large populations of neurons, are increasingly used to uncover large-scale mechanisms of brain rhythms in both health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite they are considered low-dimensional in comparison to micro-scale neural network models, with regards to understanding the relationship between parameters and dynamics NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. We need alternative methods to characterise the dynamics of NMMs in high dimensional parameter spaces. The primary aim of this thesis is to develop a method to explore and analyse the high dimensional parameter space of these mathematical models. We develop an approach based on statistics and machine learning methods called decision tree mapping (DTM). This method is used to analyse the parameter space of a mathematical model by studying all the parameters simultaneously. With this approach, the parameter space can efficiently be mapped in high dimension. We have used measures linked with this method to determine which parameters play a key role in the output of the model. This approach recursively splits the parameter space into smaller subspaces with an increasing homogeneity of dynamics. The concepts of decision tree learning, random forest, measures of importance, statistical tests and visual tools are introduced to explore and analyse the parameter space. We introduce formally the theoretical background and the methods with examples. The DTM approach is used in three distinct studies to: • Identify the role of parameters on the dynamic model. For example, which parameters have a role in the emergence of seizure dynamics? • Constrain the parameter space, such that regions of the parameter space which give implausible dynamic are removed. • Compare the parameter sets to fit different groups. How does the thalamocortical connectivity of people with and without epilepsy differ? We demonstrate that classical studies have not taken into account the complexity of the parameter space. DTM can easily be extended to other fields using mathematical models. We advocate the use of this method in the future to constrain high dimensional parameter spaces in order to enable more efficient, person-specific model calibration.
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Arat, Seda. "A Mathematical Model of a Denitrification Metabolic Network in Pseudomonas aeruginosa." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/46208.
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Lake Erie, one of the Great Lakes in North America, has witnessed recurrent summertime low oxygen dead zones for decades. This is a yearly phenomenon that causes microbial production of the greenhouse gas nitrous oxide from denitrification. Complete denitrification is a microbial process of reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and greenhouse gas nitrous oxide. After scanning the microbial community in Lake Erie, Pseudomonas aeruginosa is decided to be examined, not because it is abundant in Lake Erie, but because it can perform denitrification under anaerobic conditions. This study focuses on a mathematical model of the metabolic network in Pseudomonas aeruginosa under denitrification and testable hypotheses generation using polynomial dynamical systems and stochastic discrete dynamical systems. Analysis of the long-term behavior of the system changing the concentration level of oxygen, nitrate, and phosphate suggests that phosphate highly affects the denitrification performance of the network.Master of Science
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Wei, Zheng S. M. Massachusetts Institute of Technology. "Critical enhancements of a dynamic traffic assignment model for highly congested, complex urban network." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/58283.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 109-115).
To accurately replicate the highly congested traffic situation of a complex urban network, significant challenges are posed to current simulation-based dynamic traffic assignment (DTA) models. This thesis discusses these challenges and corresponding solutions with consideration of model accuracy and computational efficiency. DynaMITP, an off-line mesoscopic DTA model is enhanced. Model success is achieved by several critical enhancements aimed to better capture the traffic characteristics in urban networks. A Path-Size Logit route choice model is implemented to address the overlapping routes problem. The explicit representation of lane-groups accounts for traffic delays and queues at intersections. A modified treatment of acceptance capacity is required to deal with the large number of short links in the urban network. The network coding is revised to maintain enough loader access capacity in order to avoid artificial bottlenecks. In addition, the impacts of bicycles and pedestrians on automobile traffic is modeled by calibrating dynamic road segment capacities. The enhanced model is calibrated and applied to a case study network extracted from the city of Beijing, China. Data used in the calibration include sensor counts and floating car travel time. The improvements of the model performance are indicated by promising results from validation tests.
by Zheng Wei.
S.M.
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Cupertino, Thiago Henrique. "Machine learning via dynamical processes on complex networks." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032014-154520/.
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Extracting useful knowledge from data sets is a key concept in modern information systems. Consequently, the need of efficient techniques to extract the desired knowledge has been growing over time. Machine learning is a research field dedicated to the development of techniques capable of enabling a machine to \"learn\" from data. Many techniques have been proposed so far, but there are still issues to be unveiled specially in interdisciplinary research. In this thesis, we explore the advantages of network data representation to develop machine learning techniques based on dynamical processes on networks. The network representation unifies the structure, dynamics and functions of the system it represents, and thus is capable of capturing the spatial, topological and functional relations of the data sets under analysis. We develop network-based techniques for the three machine learning paradigms: supervised, semi-supervised and unsupervised. The random walk dynamical process is used to characterize the access of unlabeled data to data classes, configuring a new heuristic we call ease of access in the supervised paradigm. We also propose a classification technique which combines the high-level view of the data, via network topological characterization, and the low-level relations, via similarity measures, in a general framework. Still in the supervised setting, the modularity and Katz centrality network measures are applied to classify multiple observation sets, and an evolving network construction method is applied to the dimensionality reduction problem. The semi-supervised paradigm is covered by extending the ease of access heuristic to the cases in which just a few labeled data samples and many unlabeled samples are available. A semi-supervised technique based on interacting forces is also proposed, for which we provide parameter heuristics and stability analysis via a Lyapunov function. Finally, an unsupervised network-based technique uses the concepts of pinning control and consensus time from dynamical processes to derive a similarity measure used to cluster data. The data is represented by a connected and sparse network in which nodes are dynamical elements. Simulations on benchmark data sets and comparisons to well-known machine learning techniques are provided for all proposed techniques. Advantages of network data representation and dynamical processes for machine learning are highlighted in all casesA extração de conhecimento útil a partir de conjuntos de dados é um conceito chave em sistemas de informação modernos. Por conseguinte, a necessidade de técnicas eficientes para extrair o conhecimento desejado vem crescendo ao longo do tempo. Aprendizado de máquina é uma área de pesquisa dedicada ao desenvolvimento de técnicas capazes de permitir que uma máquina \"aprenda\" a partir de conjuntos de dados. Muitas técnicas já foram propostas, mas ainda há questões a serem reveladas especialmente em pesquisas interdisciplinares. Nesta tese, exploramos as vantagens da representação de dados em rede para desenvolver técnicas de aprendizado de máquina baseadas em processos dinâmicos em redes. A representação em rede unifica a estrutura, a dinâmica e as funções do sistema representado e, portanto, é capaz de capturar as relações espaciais, topológicas e funcionais dos conjuntos de dados sob análise. Desenvolvemos técnicas baseadas em rede para os três paradigmas de aprendizado de máquina: supervisionado, semissupervisionado e não supervisionado. O processo dinâmico de passeio aleatório é utilizado para caracterizar o acesso de dados não rotulados às classes de dados configurando uma nova heurística no paradigma supervisionado, a qual chamamos de facilidade de acesso. Também propomos uma técnica de classificação de dados que combina a visão de alto nível dos dados, por meio da caracterização topológica de rede, com relações de baixo nível, por meio de medidas de similaridade, em uma estrutura geral. Ainda no aprendizado supervisionado, as medidas de rede modularidade e centralidade Katz são aplicadas para classificar conjuntos de múltiplas observações, e um método de construção evolutiva de rede é aplicado ao problema de redução de dimensionalidade. O paradigma semissupervisionado é abordado por meio da extensão da heurística de facilidade de acesso para os casos em que apenas algumas amostras de dados rotuladas e muitas amostras não rotuladas estão disponíveis. É também proposta uma técnica semissupervisionada baseada em forças de interação, para a qual fornecemos heurísticas para selecionar parâmetros e uma análise de estabilidade mediante uma função de Lyapunov. Finalmente, uma técnica não supervisionada baseada em rede utiliza os conceitos de controle pontual e tempo de consenso de processos dinâmicos para derivar uma medida de similaridade usada para agrupar dados. Os dados são representados por uma rede conectada e esparsa na qual os vértices são elementos dinâmicos. Simulações com dados de referência e comparações com técnicas de aprendizado de máquina conhecidas são fornecidos para todas as técnicas propostas. As vantagens da representação de dados em rede e de processos dinâmicos para o aprendizado de máquina são evidenciadas em todos os casos
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Carro, Patiño Adrián. "Individual-based models of collective dynamics in socio-economic systems." Doctoral thesis, Universitat de les Illes Balears, 2016. http://hdl.handle.net/10803/396311.
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The main purpose of this thesis is to contribute to the understanding of how complex collective behaviors emerge in social and economic systems. To this end, we use a combination of mathematical analysis and computational simulations along the lines of the agent- or individual-based modeling paradigm. In particular, we focus on three main topics: opinion dynamics, herding behavior in financial markets, and language competition.
Opinion dynamics models focus on the processes of opinion formation within a society consisting of an ensemble of interacting individuals with diverse opinions. One of the main problems addressed by these models is whether these processes of opinion formation will eventually lead to the emergence of a consensus within the society or to the fragmentation of its constituent individuals into different opinion groups. We are interested here in situations where the particular issue under consideration allows for opinions to vary continuously, and thus opinions are modeled as real variables. In particular, we focus on a model consisting of two mechanisms or rules for the evolution of the agents' opinions: a mechanism of social influence, by which two interacting agents reach a compromise at the midpoint opinion, and a mechanism of homophily, by which two agents do only interact if their opinion difference is less than a given threshold value. In this context, we study the influence of the initial distribution of opinions in the asymptotic solution of the model.
Financial time series are characterized by a number of stylized facts or non-Gaussian statistical regularities found across a wide range of markets, assets and time periods, such as volatility clustering or fat-tailed distributions of returns. A growing number of contributions based on heterogeneous interacting agents have interpreted these stylized facts as the macroscopic outcome of the diversity among the economic actors, and the interplay and connections between them. In particular, we focus here on a stochastic model of information transmission in financial markets based on a competition between pairwise copying interactions between market agents (herding behavior) and random changes of state (idiosyncratic behavior). On the one hand, we develop a generalization of this herding model accounting for the arrival of information from external sources, and study the influence of this incoming information on the market. On the other hand, we study a network-embedded version of the herding model and focus on the influence of the underlying topology of interactions on the asymptotic behavior of the system.
Language competition models address the dynamics of language use in multilingual social systems due to social interactions. The main goal of these models is to distinguish between the interaction mechanisms that lead to the coexistence of different languages and those leading to the extinction of all but one of them. While traditionally conceptualized as a property of the speaker, it has been recently proposed that the use of a language can be more clearly described as a feature of the relationship between two speakers ---a link state--- than as an attribute of the speakers themselves ---a node state---. Inspired by this link-state perspective, we first develop a coevolving model that couples a majority rule dynamics of link states with the evolution of the network topology due to random rewiring of links in a local minority. Finally, we develop a model where the coupled dynamics of language use, as a property of the links between speakers, and language preference, as a property of the speakers themselves, are considered in a fixed network topology.El propósito principal de esta tesis es el de contribuir a la comprensión del modo en el que comportamientos colectivos complejos emergen en sistemas sociales y económicos. En particular, nos centramos en tres temas principales: dinámica de opiniones, comportamiento gregario en mercados financieros y competición lingüística. Los modelos de dinámica de opiniones se centran en los procesos de formación de opiniones en el seno de una sociedad compuesta por un conjunto de individuos en interacción y con opiniones diversas. Uno de los principales problemas abordados por estos modelos es el de determinar si estos procesos de formación de opiniones llevan a la emergencia de un consenso en la sociedad, o si llevan a la segregación de los individuos en diferentes grupos. Nos interesamos aquí por situaciones en las que el asunto que se discute permite la existencia de un contínuo de opiniones y por tanto las opiniones pueden ser modeladas como variables reales. En particular, nos centramos en un modelo consistente en dos mecanismos para la evolución de las opiniones: un mecanismo de influencia social, por el cual dos agentes interaccionantes llegan a un compromiso en el punto medio entre sus opiniones, y un mecanismo de homofilia, por el cual dos agentes interaccionan únicamente si la diferencia entre sus opiniones es inferior a un cierto umbral. En este contexto, estudiamos la influencia de la distribución inicial de opiniones. Las series temporales financieras están caracterizadas por una serie de hechos estilizados o regularidades estadísticas no gaussianas observadas en un amplio rango de mercados, activos y períodos temporales, como el agrupamiento de la volatilidad o las distribuciones de retornos con colas pesadas. Un número creciente de contribuciones basadas en agentes heterogéneos en interacción han venido a ofrecer una interpretación de estos hechos estilizados como el resultado emergente de la diversidad entre actores económicos y de las interacciones y conexiones entre ellos. En particular, nos centramos aquí en un modelo estocástico de transmisión de información en mercados financieros basado en una competición entre interacciones de copia a pares entre agentes de mercado (comportamiento gregario) y cambios de estado aleatorios (comportamiento idiosincrático). Por un lado, desarrollamos una generalización de este modelo de comportamiento gregario para tener en cuenta la llegada de información desde fuentes externas y estudiamos la influencia de esta información entrante en el mercado. Por otro lado, estudiamos una versión en red del modelo de comportamiento gregario y nos centramos en la influencia de la topología subyacente en el comportamiento asintótico del sistema. Los modelos de competición lingüística abordan la dinámica del uso de lenguas en sistemas sociales multilingües debida a interacciones sociales. El principal objetivo de estos modelos es el de diferenciar entre aquellos mecanismos de interacción que llevan a la coexistencia de diferentes lenguas y aquellos que llevan a la extinción de todas menos una. Aunque tradicionalmente se ha conceptualizado como una propiedad del hablante, recientemente se ha propuesto que el uso de una lengua puede ser más claramente descrito como una propiedad de la relación entre dos hablantes ---un estado del enlace--- que como una propiedad de los hablantes ---un estado del nodo---. Inspirados por esta perspectiva, desarrollamos primero un modelo de coevolución que acopla una dinámica de estados en los enlaces basada en una regla de mayoría con la evolución de la topología de la red debida al re-enlace aleatorio de enlaces en una minoría local. Finalmente, desarrollamos un modelo en el que las dinámicas acopladas de uso de la lengua, como propiedad de los enlaces entre hablantes, y preferencia lingüística, como propiedad de los hablantes mismos, son consideradas en una topología de red fija.
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Kamapantula, Bhanu K. "In-silico Models for Capturing the Static and Dynamic Characteristics of Robustness within Complex Networks." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/4049.
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Understanding the role of structural patterns within complex networks is essential to establish the governing principles of such networks. Social networks, biological networks, technological networks etc. can be considered as complex networks where information processing and transport plays a central role. Complexity in these net works can be due to abstraction, scale, functionality and structure. Depending on the abstraction each of these can be categorized further. Gene regulatory networks are one such category of biological networks. Gene regulatory networks (GRNs) are assumed to be robust under internal and external perturbations. Network motifs such as feed-forward loop motif and bifan motif are believed to play a central role functionally in retaining GRN behavior under lossy conditions. While the role of static characteristics like average shortest path, density, degree centrality among other topological features is well documented by the research community, the structural role of motifs and their dynamic characteristics are not xiii well understood. Wireless sensor networks in the last decade were intensively studied using network simulators. Can we use in-silico experiments to understand biological network topologies better? Does the structure of these motifs have any role to play in ensuring robust information transport in such networks? How do their static and dynamic roles differ? To understand these questions, we use in-silico network models to capture the dynamic characteristics of complex network topologies. Developing these models involve network mapping, sink selection strategies and identifying metrics to capture robust system behavior. Further, I studied the dynamic aspect of network characteristics using variation in network information flow under perturbations defined by lossy conditions and channel capacity. We use machine learning techniques to identify significant features that contribute to robust network performance. Our work demonstrates that although the structural role of feed-forward loop motif in signal transduction within GRNs is minimal, these motifs stand out under heavy perturbations.
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Nath, Madhurima. "Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/86841.
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Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the number of samples required for an accurate estimate increases with system size. Instead, an adaptive method using Bernstein polynomials as kernel density estimators proves useful. Network reliability has a wide range of applications ranging from epidemiology to statistical physics, depending on the description of the functionality. For example, it serves as a measure to study the sensitivity of the outbreak of an infectious disease on a network to the structure of the network. It can also be used to identify important dynamics-induced contagion clusters in international food trade networks. Further, it is analogous to the partition function of the Ising model which provides insights to the interpolation between the low and high temperature limits.Ph. D.
The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network reliability. The network reliability is a finite degree polynomial which provides the probability of observing a certain configuration for a diffusive process on networks. Examples of such processes analyzed here are outbreak of an epidemic in a population, spread of an invasive species through international trade of commodities and spread of a perturbation in a physical system with discrete magnetic spins. Network reliability is a novel tool which can be used to compare the efficiency of network models with the observed data, to find important components of the system as well as to estimate the functions of thermodynamic state variables.
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Ahn, Sungwoo. "Transient and Attractor Dynamics in Models for Odor Discrimination." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1280342970.
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Powell, Sean K. "A quantitative study of diffusion in quasi-periodic fibre networks and complex porous media." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/92506/12/92506%28thesis%29.pdf.
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Diffusion is the fundamental process behind many molecular phenomena such as the mixing of substances. Its physical basis is the random motion of particles in a fluid. In complex porous media, diffusion is restricted by interactions with internal structures. In this work, we present studies of restricted diffusion that aim to efficiently produce quantitative models for obtaining detailed information about the morphology of biological porous media from diffusion tensor imaging experiments. We achieved this by developing a Langevin dynamics algorithm to provide physically realistic modelling of water/barrier interactions and the Lattice-Path Count algorithm to enumerate all available particle trajectories to evaluate molecular transport properties. We also performed diffusion tensor imaging experiments of the fibre networks of tissue engineering scaffolds. The findings of this thesis provide further insight into the physics underlying restricted diffusion.
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Xin, Ying. "Complex Dynamical Systems: Definitions of Entropy, Proliferation of Epithelia and Spread of Infections and Information." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1522955730251256.
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Schmeltzer, Christian [Verfasser], Sten [Akademischer Betreuer] Rüdiger, Alexandre [Akademischer Betreuer] Kihara, and Enrique Alvarez [Akademischer Betreuer] Lacalle. "Dynamical properties of neuronal systems with complex network structure / Christian Schmeltzer. Gutachter: Sten Rüdiger ; Alexandre Kihara ; Enrique Alvarez Lacalle." Berlin : Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://d-nb.info/1096286297/34.
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Alqithami, Saad. "Network Organization Paradigm." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1293.
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In a complex adaptive system, diverse agents perform various actions without adherence to a predefined structure. The achievement of collaborative actions will be the result of continual interactions among them that shape a dynamic network. Agents may form an ad hoc organization based on the dynamic network of interactions for the purpose of achieving a long-term objective, which we termed a Network Organization (NO). Fervent and agile communication on social networking sites provides opportunities for potential issues to trigger individuals into individual actions as well as the attraction and mobilization of like-minded individuals into an NO that is both physically and virtually emergent. Examples are the rapid pace of Arab Spring proliferation and the diffusion rate of the Occupy Movement. We are motivated by a spontaneously formed NO as well as the quality of plasticity that enables the organization to change rapidly to describe an NO. Thus, we present a paradigm that serves as a reference model for organizations of socially networked individuals. This paradigm suggests modular components that can be combined to form an ad hoc network organization of agents. We touch on how this model accounts for external change in an environment through internal adjustment. For the predominant influences of the network substrate in an NO, multiple effects of it have an impact on the NO behaviors and directions. We envisioned several dimensions of such effects to include synergy, social capital, externality, influence, etc. A special focus in this work is measuring synergy and social capital as two predominant network effects. Synergy is perceived as different modalities of compatibility among agents when performing a set of coherent and correspondingly different actions. When agents are under no structural obligation to contribute, synergy is quantified through multiple forms of serendipitous agent chosen benevolence among them. The approach is to measure four types of benevolence and the pursuant synergies stemming from agent interactions. Social capital is another effect of networking that describes the accumulation of positive values of social flow and perceived trust plus abundance of communication over the common topic of NO. We provide measurement of social capital based on an agents’ expected benevolence. We examine those two effects in two different case studies — one case of a virtual organization and another of a real world terrorist organization — that best illustrate the main tenets of our conceptualization.
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Magri, Glaucia Ligia Kelly Priscilla Midori Funakura Gondo. "Simulação baseada em agentes para a análise do comportamento do contribuinte quanto à sonegação: um modelo de evasão fiscal em redes complexas aleatórias." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-05052015-233144/.
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O tributo é cobrado dos cidadãos porque ele é o custo do contrato social, um acordo entre pessoas para uma vida em sociedade. Diretamente, há um retorno para a sociedade na forma de serviços públicos; indiretamente, é um instrumento do governo para intervir na economia, como na condução de política fiscal e na redução das desigualdades sociais. Um contribuinte está obrigado a pagá-lo porque essa obrigação decorre de uma regra formal (lei) determinada pelas instituições públicas; e estas são criadas para estruturar as interações humanas, por meio de incentivos. Pretendemos apresentar uma alternativa na análise da relação cidadão-governo na arrecadação tributária e no custo relacionado ao combate à sonegação (por meio da fiscalização de tributos), tendo como objeto de estudo a obediência tributária no âmbito da escolha do contribuinte. Propõe-se uma modelagem baseada em agentes para a análise do comportamento do contribuinte quanto à sonegação, apresentando um modelo de evasão fiscal em redes complexas aleatórias. Nossa principal contribuição é incluir a evolução da percepção do contribuinte sobre as ações de fiscalização do governo em um modelo com abordagem em complexidade. A partir da convergência de fatores individuais (risco aceitável), sociais (interação social) e ambientais (dinâmica com o governo) na adoção de comportamento de sonegação pelo contribuinte, serão comparadas a evolução da evasão fiscal (quantidade de declarações de imposto recebidas) e a da fiscalização do governo (número aproximado de contribuintes fiscalizados) a fim de compreender a dinâmica entre contribuinte e governo.Taxes are charged to citizens because they are the cost of the social contract, that is an agreement between people to a life in society. Directly, they convert to society into public goods and services; indirectly, are a means of the government to intervene in the economy, for example, the reduction of social inequalities in the regulation of domestic and foreign trade etc. A taxpayer is required to pay a tax because of a formal rule (law) determined by the public institutions. Institutions are created to structure human interactions through incentives. Our intention is to present an alternative analysis of the state-citizen relations in tax revenue and cost related to tax avoidance and tax audit. The object of study is tax compliance under taxpayer behavior. We present an agent-based simulation for analyzing the evasion behavior of the taxpayer, a model of tax evasion in random complex networks. Our main contribution is to include the evolution of the taxpayer perceptions on the government actions oversight in a model with the complexity approach. From the convergence of individual aspects (agents decision under acceptable risk), social influences (social interaction) and environmental factors (dynamic with the government) to tax evasion behavior, is analized the evolution of tax compliance (by tax declaration) and tax audit in order to understand the dynamic behavior between taxpayer and government.
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Johnson, Sandra. "Integrated Bayesian network frameworks for modelling complex ecological issues." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/32002/1/Sandra_Johnson_Thesis.pdf.
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Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager.
Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature.
This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling.
The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups.
In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis.
The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.
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Wang, Zhen. "Human disease-behavior interactions on complex networks models: incorporating evolutionary game into epidemiology." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/22.
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In the past decade, the study of disease dynamics on complex networks has attracted great attention from both theoretical and empirical viewpoints. Under such a framework, people try to predict the outbreak of disease and propose immunization mechanisms. However, this framework possesses a limitation, which makes it inconsistent with realistic cases. That is, this framework does not consider the impact of human behavior or decision-making progress on disease dynamic characters and prevention measures. To further resolve this problem, we in this thesis propose behavioral epidemiology based on game theory, which involves the interactions between disease dynamics and human behavior in complex networks. Motivated by realistic cases, we proceed with the research from theoretical models and consider the following aspects. We .rst re-construct a scheme of risk perception incorporating local and global information and show that this new evaluation scenario not only promotes vaccination uptake, but also eliminates the disease spreading. This interesting .nding could be attributed to the positive feedback mechanism between vaccination uptake and disease spreading. Then, we introduce a self-protection measure, which, due to low cost, can only provide temporary protection. By simulations and analysis we show that this measure leads to multiple e.ects: contrary with cases of low (high) e.ciency and cost of the self-protection measure, middle values drive more infection and larger cost, which is related to the loss of positive feedback between prevention measures and disease propagation. Subsequently, another scheme of adaptive protection is proposed, where a healthy agent can cut the connection with infected ones. We .nd that adaptive protection can e.ectively eradicate the disease and result in an optimal level of pruning infected links. Di.erent from these proposals focusing on individual interest, we lastly study a subsidy policy from the viewpoint of population bene.t. We .nd that disease can be well controlled with an increase of the vaccination level, while the total expense reduces. Taken together, these .ndings of the thesis further demonstrate that the interplay between disease dynamics and human behavior plays an important role in the control of diseases. The models presented in this thesis, especially combining with empirical data, may serve as a foundation for further investigation of the subject in the future.
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Wu, Sichao. "Computational Framework for Uncertainty Quantification, Sensitivity Analysis and Experimental Design of Network-based Computer Simulation Models." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78764.
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When capturing a real-world, networked system using a simulation model, features are usually omitted or represented by probability distributions. Verification and validation (V and V) of such models is an inherent and fundamental challenge. Central to V and V, but also to model analysis and prediction, are uncertainty quantification (UQ), sensitivity analysis (SA) and design of experiments (DOE). In addition, network-based computer simulation models, as compared with models based on ordinary and partial differential equations (ODE and PDE), typically involve a significantly larger volume of more complex data. Efficient use of such models is challenging since it requires a broad set of skills ranging from domain expertise to in-depth knowledge including modeling, programming, algorithmics, high- performance computing, statistical analysis, and optimization. On top of this, the need to support reproducible experiments necessitates complete data tracking and management. Finally, the lack of standardization of simulation model configuration formats presents an extra challenge when developing technology intended to work across models. While there are tools and frameworks that address parts of the challenges above, to the best of our knowledge, none of them accomplishes all this in a model-independent and scientifically reproducible manner.
In this dissertation, we present a computational framework called GENEUS that addresses these challenges. Specifically, it incorporates (i) a standardized model configuration format, (ii) a data flow management system with digital library functions helping to ensure scientific reproducibility, and (iii) a model-independent, expandable plugin-type library for efficiently conducting UQ/SA/DOE for network-based simulation models. This framework has been applied to systems ranging from fundamental graph dynamical systems (GDSs) to large-scale socio-technical simulation models with a broad range of analyses such as UQ and parameter studies for various scenarios. Graph dynamical systems provide a theoretical framework for network-based simulation models and have been studied theoretically in this dissertation. This includes a broad range of stability and sensitivity analyses offering insights into how GDSs respond to perturbations of their key components. This stability-focused, structure-to-function theory was a motivator for the design and implementation of GENEUS.
GENEUS, rooted in the framework of GDS, provides modelers, experimentalists, and research groups access to a variety of UQ/SA/DOE methods with robust and tested implementations without requiring them to necessarily have the detailed expertise in statistics, data management and computing. Even for research teams having all the skills, GENEUS can significantly increase research productivity.Ph. D.
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Hu, Qiong. "Statistical parametric speech synthesis based on sinusoidal models." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28719.
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This study focuses on improving the quality of statistical speech synthesis based on sinusoidal models. Vocoders play a crucial role during the parametrisation and reconstruction process, so we first lead an experimental comparison of a broad range of the leading vocoder types. Although our study shows that for analysis / synthesis, sinusoidal models with complex amplitudes can generate high quality of speech compared with source-filter ones, component sinusoids are correlated with each other, and the number of parameters is also high and varies in each frame, which constrains its application for statistical speech synthesis. Therefore, we first propose a perceptually based dynamic sinusoidal model (PDM) to decrease and fix the number of components typically used in the standard sinusoidal model. Then, in order to apply the proposed vocoder with an HMM-based speech synthesis system (HTS), two strategies for modelling sinusoidal parameters have been compared. In the first method (DIR parameterisation), features extracted from the fixed- and low-dimensional PDM are statistically modelled directly. In the second method (INT parameterisation), we convert both static amplitude and dynamic slope from all the harmonics of a signal, which we term the Harmonic Dynamic Model (HDM), to intermediate parameters (regularised cepstral coefficients (RDC)) for modelling. Our results show that HDM with intermediate parameters can generate comparable quality to STRAIGHT. As correlations between features in the dynamic model cannot be modelled satisfactorily by a typical HMM-based system with diagonal covariance, we have applied and tested a deep neural network (DNN) for modelling features from these two methods. To fully exploit DNN capabilities, we investigate ways to combine INT and DIR at the level of both DNN modelling and waveform generation. For DNN training, we propose to use multi-task learning to model cepstra (from INT) and log amplitudes (from DIR) as primary and secondary tasks. We conclude from our results that sinusoidal models are indeed highly suited for statistical parametric synthesis. The proposed method outperforms the state-of-the-art STRAIGHT-based equivalent when used in conjunction with DNNs. To further improve the voice quality, phase features generated from the proposed vocoder also need to be parameterised and integrated into statistical modelling. Here, an alternative statistical model referred to as the complex-valued neural network (CVNN), which treats complex coefficients as a whole, is proposed to model complex amplitude explicitly. A complex-valued back-propagation algorithm using a logarithmic minimisation criterion which includes both amplitude and phase errors is used as a learning rule. Three parameterisation methods are studied for mapping text to acoustic features: RDC / real-valued log amplitude, complex-valued amplitude with minimum phase and complex-valued amplitude with mixed phase. Our results show the potential of using CVNNs for modelling both real and complex-valued acoustic features. Overall, this thesis has established competitive alternative vocoders for speech parametrisation and reconstruction. The utilisation of proposed vocoders on various acoustic models (HMM / DNN / CVNN) clearly demonstrates that it is compelling to apply them for the parametric statistical speech synthesis.
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Molter, Colin. "Storing information through complex dynamics in recurrent neural networks." Doctoral thesis, Universite Libre de Bruxelles, 2005. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211039.
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The neural net computer simulations which will be presented here are based on the acceptance of a set of assumptions that for the last twenty years have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First of all, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural net should be coded in some way or another in one of the dynamical attractors of the brain and retrieved by stimulating the net so as to trap its dynamics in the desired item's basin of attraction. The second view shared by neural net researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The last assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli and being able to easily switch from one of these potential attractors to another in response to any coming stimulus.In this thesis, it is shown experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the back, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause but the consequence of the learning. However, it appears as an helpful consequence that widens the net's encoding capacity. To learn the information to be stored, an unsupervised Hebbian learning algorithm is introduced. By leaving the semantics of the attractors to be associated with the feeding data unprescribed, promising results have been obtained in term of storing capacity.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
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Padmanabhan, Sathya. "Broad-band space conservative on wafer network analyzer calibrations with more complex SOLT definitions." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000318.
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Imam, Ayad Tareq. "Relative-fuzzy : a novel approach for handling complex ambiguity for software engineering of data mining models." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/3909.
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There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data.
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Stolk, Henk. "Emergent models in hierarchical and distributed simulation of complex systems : with applications to ecosystem and genetic network modelling /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19095.pdf.
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Nguyen, Viet Pham Quoc. "A characteristic-preserving technique for lossless and invertible simpification of large complex three-dimensional Triangulated Irregular Network models." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/78127.
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This dissertation describes a characteristic-preserving, input-tolerating, lossless and invertible TIN simplification technique, called AQSlim. AQSlim is superior to previously published techniques because it possesses this full set of important characteristics that existing methods can only partially match, and performs comparably or better to existing methods on metrics such as input model size, error, visual fidelity and topological preservation. AQSlim can also be applied as the basis of a lossless compression storage format for TIN models