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Tesi sul tema "Stochastic neural networks"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 25 luglio 2025

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1

Pensuwon, Wanida. "Stochastic dynamic hierarchical neural networks." Thesis, University of Hertfordshire, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366030.

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2

Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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Abstract (sommario):
The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory f
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3

CAMPOS, LUCIANA CONCEICAO DIAS. "PERIODIC STOCHASTIC MODEL BASED ON NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17076@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Processo Estocástico é um ramo da teoria da probabilidade onde se define um conjunto de modelos que permitem o estudo de problemas com componentes aleatórias. Muitos problemas reais apresentam características complexas, tais como não-linearidade e comportamento caótico, que necessitam de modelos capazes de capturar as reais características do problema para obter um tratamento apropriado. Porém, os modelos existentes ou são lineares, cuja aplicabilidade a esses problemas pode ser inadequada, ou necessitam de uma formulação complex
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4

Ling, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.

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Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems’ natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in
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5

Zhao, Jieyu. "Stochastic bit stream neural networks : theory, simulations and applications." Thesis, Royal Holloway, University of London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338916.

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6

Hyland, P. "On the implementation of neural networks using stochastic arithmetic." Thesis, Bangor University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306224.

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7

Todeschi, Tiziano. "Calibration of local-stochastic volatility models with neural networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23052/.

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During the last twenty years several models have been proposed to improve the classic Black-Scholes framework for equity derivatives pricing. Recently a new model has been proposed: Local-Stochastic Volatility Model (LSV). This model considers volatility as the product between a deterministic and a stochastic term. So far, the model choice was not only driven by the capacity of capturing empirically observed market features well, but also by the computational tractability of the calibration process. This is now undergoing a big change since machine learning technologies offer new perspectives
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8

陳穎志 and Wing-chi Chan. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241475.

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9

Chan, Wing-chi. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22925843.

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10

Rising, Barry John Paul. "Hardware architectures for stochastic bit-stream neural networks : design and implementation." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326219.

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11

Zhu, Huaiyu. "Neural networks and adaptive computers : theory and methods of stochastic adaptive computation." Thesis, University of Liverpool, 1993. http://eprints.aston.ac.uk/365/.

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Abstract (sommario):
This thesis studies the theory of stochastic adaptive computation based on neural networks. A mathematical theory of computation is developed in the framework of information geometry, which generalises Turing machine (TM) computation in three aspects - It can be continuous, stochastic and adaptive - and retains the TM computation as a subclass called "data processing". The concepts of Boltzmann distribution, Gibbs sampler and simulated annealing are formally defined and their interrelationships are studied. The concept of "trainable information processor" (TIP) - parameterised stochastic mappi
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12

Balkhair, Khaled Saeed. "Artificial neural networks and conditional stochastic simulations for characterization of aquifer heterogeneity." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/284451.

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Abstract (sommario):
Although it is one of the most difficult tasks in hydrology, delineation of aquifer heterogeneity is essential for accurate simulation of groundwater flow and transport. There are various approaches used to delineate aquifer heterogeneity from a limited data set, and each has its own difficulties and drawbacks. The inverse problem is usually used for estimating different hydraulic properties (e.g. transmissivity) from scattered measurements of these properties, as well as hydraulic head. Difficulties associated with this approach are issues of indentifiability, uniqueness, and stability. The I
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13

Carelli, Pedro Valadão. "Modelagem estocástica de neurônios e sua interação em tempo real com neurônios biológicos." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-04092008-154828/.

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Desenvolvemos um modelo estocástico da atividade elétrica de um neurônio motor do gânglio estomatogástrico de crustáceos, a partir de um modelo determinístico eletrofisiologicamente plausível. Com isso recuperamos características da dinâmica neural sempre observadas em neurônios isolados, tais como irregularidades nos padrões de disparos que não são reproduzidas pelo modelo determinístico original. Implementamos otimizações e simplificações no método numérico de simulação estocástica que permitiram rodar a simulação em tempo real para interagir modelos computacionais com neurônios biológicos,
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14

Orr, Genevieve Beth. "Dynamics and algorithms for stochastic search /." Full text open access at:, 1995. http://content.ohsu.edu/u?/etd,197.

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15

Feurstein, Markus, and Martin Natter. "Neural networks, stochastic dynamic programming and a heuristic for valuing flexible manufacturing systems." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/1106/1/document.pdf.

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We compare the use of stochastic dynamic programming (SDP), Neural Networks and a simple approximation rule for calculating the real option value of a flexible production system. While SDP yields the best solution to the problem, it is computationally prohibitive for larger settings. We test two approximations of the value function and show that the results are comparable to those obtained via SDP. These methods have the advantage of a high computational performance and of no restrictions on the type of process used. Our approach is not only useful for supporting large investment decisions, bu
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16

Haverinen, J. (Janne). "Adaptation through a Stochastic Evolutionary Neuron Migration Process." Doctoral thesis, University of Oulu, 2004. http://urn.fi/urn:isbn:9514273079.

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Abstract Artificial Life is an interdisciplinary scientific and engineering enterprise investigating the fundamental properties of living systems through the simulation and synthesis of life-like processes in artificial media. One of the avenues of investigation is autonomous robots and agents. Mimicking of the growth and adaptation of a biological neural circuit in an artificial medium is a challenging task owing to our limited knowledge of the complex process taking place in a living organism. By combining several developmental mechanisms, including the chemical, mechanical, genetic, and el
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17

Tran-Canh, Dung. "Simulating the flow of some non-Newtonian fluids with neural-like networks and stochastic processes." University of Southern Queensland, Faculty of Engineering and Surveying, 2004. http://eprints.usq.edu.au/archive/00001518/.

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The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-lay
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18

Boscarino, Andrea. "Deep Learning Models with Stochastic Targets: an Application for Transprecision Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20078/.

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Abstract (sommario):
Il presente elaborato di tesi è parte di un ampio progetto finanziato dall’Unione Europea, sotto il programma Horizon 2020 per la ricerca e l’innovazione, Open Transprecision Computing (OPRECOMP). Il progetto, della durata di 4 anni, punta a superare l’assunto conservativo secondo cui ogni calcolo compiuto da sistemi e applicazioni computazionali debba essere eseguito utilizzando la massima precisione numerica. Tale assunto è finora risultato sensato in vista di un’efficienza computazionale sempre migliore col passare del tempo, secondo la legge di Moore. Com’è noto, nell’era attuale tale legg
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19

Ahmadian, Mansooreh. "Hybrid Modeling and Simulation of Stochastic Effects on Biochemical Regulatory Networks." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99481.

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Abstract (sommario):
A complex network of genes and proteins governs the robust progression through cell cycles in the presence of inevitable noise. Stochastic modeling is viewed as a key paradigm to study the effects of intrinsic and extrinsic noise on the dynamics of biochemical networks. A detailed quantitative description of such complex and multiscale networks via stochastic modeling poses several challenges. First, stochastic models generally require extensive computations, particularly when applied to large networks. Second, the accuracy of stochastic models is highly dependent on the quality of the paramet
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20

Myers, James William. "Stochastic algorithms for learning with incomplete data an application to Bayesian networks /." Full text available online (restricted access), 1999. http://images.lib.monash.edu.au/ts/theses/Myers.pdf.

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21

Droh, Erik. "T-Distributed Stochastic Neighbor Embedding Data Preprocessing Impact on Image Classification using Deep Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237422.

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Image classification in Machine Learning encompasses the task of identification of objects in an image. The technique has applications in various areas such as e-commerce, social media and security surveillance. In this report the author explores the impact of using t-Distributed Stochastic Neighbor Embedding (t-SNE) on data as a preprocessing step when classifying multiple classes of clothing with a state-of-the-art Deep Convolutional Neural Network (DCNN). The t-SNE algorithm uses dimensionality reduction and groups similar objects close to each other in three-dimensional space. Extracting t
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22

Antrobus, Alexander Dennis. "Achieving baseline states in sparsely connected spiking-neural networks: stochastic and dynamic approaches in mathematical neuroscience." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/19949.

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Networks of simple spiking neurons provide abstract models for studying the dynamics of biological neural tissue. At the expense of cellular-level complexity, they are a frame-work in which we can gain a clearer understanding of network-level dynamics. Substantial insight can be gained analytically, using methods from stochastic calculus and dynamical systems theory. This can be complemented by data generated from computational simulations of these models, most of which benefit easily from parallelisation. One cubic millimetre of mammalian cortical tissue can contain between fifty and one-hun
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23

Yang, Jidong. "Road crack condition performance modeling using recurrent Markov chains and artificial neural networks." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000567.

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24

Weller, Tobias [Verfasser], and Y. [Akademischer Betreuer] Sure-Vetter. "Learning Latent Features using Stochastic Neural Networks on Graph Structured Data / Tobias Weller ; Betreuer: Y. Sure-Vetter." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1230475656/34.

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25

Larsson, Sofia. "A Study of the Loss Landscape and Metastability in Graph Convolutional Neural Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273622.

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Many novel graph neural network models have reported an impressive performance on benchmark dataset, but the theory behind these networks is still being developed. In this thesis, we study the trajectory of Gradient descent (GD) and Stochastic gradient descent (SGD) in the loss landscape of Graph neural networks by replicating Xing et al. [1] study for feed-forward networks. Furthermore, we empirically examine if the training process could be accelerated by an optimization algorithm inspired from Stochastic gradient Langevin dynamics and what effect the topology of the graph has on the converg
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26

Kromer, Justus Alfred. "Noise in adaptive excitable systems and small neural networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17683.

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Neuronen sind erregbare Systeme. Ihre Antwort auf Anregungen oberhalb eines bestimmten Schwellwertes sind Pulse. Häufig wird die Pulserzeugung von verschiedenen Rückkopplungsmechanismen beeinflusst, die auf langsamen Zeitskalen agieren. Das kann zu Phänomenen wie Feuerraten-Adaptation, umgekehrter Feuerraten-Adaptation oder zum Feuern von Pulsen in Salven führen. Weiterhin sind Neuronen verschiedenen Rauschquellen ausgesetzt und wechselwirken mit anderen Neuronen, in neuronalen Netzen. Doch wie beeinflusst das Zusammenspiel von Rückkopplungsmechanismen, Rauschen und der Wechselwirkung mit and
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27

Koroko, Abdoulaye. "Natural gradient-based optimization methods for deep neural networks." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG068.

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La méthode du gradient stochastique est la technologie actuellement prédominante pour effectuer la phase d'entraînement des réseaux de neurones. Par rapport à une descente classique, le calcul du vrai gradient comme une moyenne sur les données est remplacé par un élément aléatoire de la somme. En présence de données massives, cette approximation audacieuse permet de diminuer le nombre d'évaluations de gradients élémentaires et d'alléger le coût de chaque itération. Le prix à payer est l'apparition d'oscillations et la lenteur de convergence souvent excessive en nombre d'itérations. L'objectif
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28

Alisar, Ibrahim. "Stochastic Modelling Of Wind Energy Generation." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614930/index.pdf.

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In this thesis work, electricty generation modeling of the wind energy -one type of the renewable energy sources- is studied. The wind energy characteristics and the distribution of wind speed in a specific region is also examined. In addition, the power curves of the wind turbines are introduced and the relationship between the wind speed and wind power is explained. The generation characteristics of the wind turbines from various types of producers are also investigated. In this study, the main wind power forecasting methods are presented and the advantages and disadvantages of the methods a
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29

Mignacco, Francesca. "Statistical physics insights on the dynamics and generalisation of artificial neural networks." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPASP074.

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L'apprentissage machine est une technologie désormais omniprésente dans notre quotidien. Toutefois, ce domaine reste encore largement empirique et ses enjeux scientifiques manquent d'une compréhension théorique profonde. Cette thèse se penche vers la découverte des mécanismes sous-tendant l'apprentissage dans les réseaux de neurones artificiels à travers le prisme de la physique statistique. Dans une première partie, nous nous intéressons aux propriétés statiques des problèmes d'apprentissage, que nous introduisons au chapitre 1.1. Dans le chapitre 1.2, nous considérons la classification d'un
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30

Russo, Elena Tea. "Fluctuation properties in random walks on networks and simple integrate and fire models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9565/.

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In questa tesi si è studiato l’insorgere di eventi critici in un semplice modello neurale del tipo Integrate and Fire, basato su processi dinamici stocastici markoviani definiti su una rete. Il segnale neurale elettrico è stato modellato da un flusso di particelle. Si è concentrata l’attenzione sulla fase transiente del sistema, cercando di identificare fenomeni simili alla sincronizzazione neurale, la quale può essere considerata un evento critico. Sono state studiate reti particolarmente semplici, trovando che il modello proposto ha la capacità di produrre effetti "a cascata" nell’attiv
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31

Boumaaza, Bouharket. "3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35510.

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Master of Science<br>Department of Geology<br>Abdelmoneam Raef<br>Matthew W. Totten<br>This work focuses on the use of advanced seismically driven technologies to estimate the distribution of key reservoir properties which mainly includes porosity and hydrocarbon reservoir pay. These reservoir properties were estimated by using a multitude of seismic attributes derived from post-stack high resolution inversions, spectral imaging and volumetric curvature. A pay model of the reservoir in the Wierman field in Ness County, Kansas is proposed. The proposed geological model is valid
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32

Ngatchou, Patrick. "Intelligent techniques for optimization and estimation /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/5827.

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33

Kozel, Tomáš. "Stochastické řízení zásobní funkce nádrže s pomocí metod umělé inteligence." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2018. http://www.nusl.cz/ntk/nusl-390282.

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The main advantage of stochastic forecasting is fan of possible value, which deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. We can categorize discharge in measurement profile as random process. Stochastic management is worked with dispersion of controlling discharge value. In thesis is described construction and evaluation of adaptive stochastic model base on fuzzy logic, neural networks and evolution algorithm, which are used stochastic forecast from forecasting models described in t
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34

Simonetti, Roberta. "Generalização e Robustez: Aprendizagem em Redes Neurais na Presença de Ruído." Universidade de São Paulo, 1997. http://www.teses.usp.br/teses/disponiveis/43/43133/tde-17122013-145626/.

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Neste trabalho investigamos o aprendizado supervisionado on-line, com ênfase nas habilidades de generalização, de redes neurais feedforward. O estudo de algoritmos de aprendizagem ótimos, no sentido da generalização, é estendido para duas diferentes classes de arquiteturas: a máquina paridade com estrutura de árvore e K unidades escondidas, e o perceptron reversed wedge, uma máquina de uma camada com função de transferência não monotônica. O papel do ruído é de fundamental importância na teoria de aprendizagem. Neste trabalho estudamos os processos com ruído que podem ser parametrizados por um
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35

Bono, Guillaume. "Deep multi-agent reinforcement learning for dynamic and stochastic vehicle routing problems." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI096.

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La planification de tournées de véhicules dans des environnements urbains denses est un problème difficile qui nécessite des solutions robustes et flexibles. Les approches existantes pour résoudre ces problèmes de planification de tournées dynamiques et stochastiques (DS-VRPs) sont souvent basés sur les mêmes heuristiques utilisées dans le cas statique et déterministe, en figeant le problème à chaque fois que la situation évolue. Au lieu de cela, nous proposons dans cette thèse d’étudier l’application de méthodes d’apprentissage par renforcement multi-agent (MARL) aux DS-VRPs en s’appuyant sur
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36

Malmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.

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Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. T
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37

Dickson, Scott M. "Stochastic neural network dynamics : synchronisation and control." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16508.

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Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain's performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson's disease. As a result, it is desirable to research under which conditions synchronisation arises in neural
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38

Botha, Marthinus Ignatius. "Modelling and simulation framework incorporating redundancy and failure probabilities for evaluation of a modular automated main distribution frame." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33345.

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Maintaining and operating manual main distribution frames is labour-intensive. As a result, Automated Main Distribution Frames (AMDFs) have been developed to alleviate the task of maintaining subscriber loops. Commercial AMDFs are currently employed in telephone exchanges in some parts of the world. However, the most significant factors limiting their widespread adoption are costeffective scalability and reliability. Therefore, an impelling incentive is provided to create a simulation framework in order to explore typical implementations and scenarios. Such a framework will allow the eva
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39

Hu, Kaitong. "Jeux différentiels stochastiques non-Markoviens etdynamiques de Langevin à champ-moyen." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX005.

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Cette thèse se compose de deux parties indépendantes et la première regroupant deux problématiques distinctes. Dans la première partie, nous étudions d’abord le problème de Principal-Agent dans des systèmes dégénérés, qui apparaissent naturellement dans des environnements à l’observation partielle où l’Agent et le Principal n’observent qu’une partie du système. Nous présentons une approche se basant sur le principe du maximum stochastique, dont le but est d’étendre les travaux existants qui utilisent le principe de la programmation dynamique dans des systèmes non-dégénérés. D’abord nous résolv
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40

Canals, Guinand Vicente José. "Implementación en hardware de sistemas de alta fiabilidad basados en metodologías estocásticas." Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84125.

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La sociedad actual demanda cada vez más aplicaciones computacionalmente exigentes y que se implementen de forma energéticamente eficiente. Esto obliga a la industria del semiconductor a mantener una continua progresión de la tecnología CMOS. No obstante, los expertos vaticinan que el fin de la era de la progresión de la tecnología CMOS se acerca, puesto que se prevé que alrededor del 2020 la tecnología CMOS llegue a su límite. Cuando ésta llegue al punto conocido como “Red Brick Wall”, las limitaciones físicas, tecnológicas y económicas no harán viable el proseguir por esta senda. Todo e
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41

Georgieva, Antoniya. "Stochastic methods and genetic algorithms for neural network learning." Thesis, University of Portsmouth, 2008. https://researchportal.port.ac.uk/portal/en/theses/stochastic-methods-and-genetic-algorithms-for-neural-network-learning(67dae83c-ec3d-4db2-875c-6e7407a4144f).html.

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This thesis presents results from the developemnt, investigation, testing and evaluation of novel meta-heuristic techniques aiming to further improve the <i>state-of-the-art </i>of algorithms for local minima free Neural Network supervised learning. Several approaches for solving Global Optimisation problems that make use of novel meta-heuristic techniques, so called Low-discrepancy Sequences, and hybrid Evolutionary Algorithms are proposed here, investigated and critically discussed. Furthermore, the novel methods are tested on a number of multimodal mathematical function optimisation problem
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42

Anisenia, Andrei. "Stochastic Search Genetic Algorithm Approximation of Input Signals in Native Neuronal Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/26220.

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The present work investigates the applicability of Genetic Algorithms (GA) to the problem of signal propagation in Native Neuronal Networks (NNNs). These networks are comprised of neurons, some of which receive input signals. The signals propagate though the network by transmission between neurons. The research focuses on the regeneration of the output signal of the network without knowing the original input signal. The computational complexity of the problem is prohibitive for the exact computation. We propose to use a heuristic approach called Genetic Algorithm. Three algorithms are develope
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43

Aguiar, Eliane Martins de. "Aplicação do Word2vec e do Gradiente descendente dstocástico em tradução automática." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/16798.

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Submitted by Eliane Martins de Aguiar (elianemart@gmail.com) on 2016-08-01T21:03:09Z No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2016-08-03T20:29:34Z (GMT) No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 6062037 bytes, checksum: 14567c2feca25a81d6942be3b8bc8a65 (MD5)<br>Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-08-23T20:12:35Z (GMT) No. of bitstreams: 1 dissertacao-ElianeMartins.pdf: 606
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Martí, Ortega Daniel. "Neural stochastic dynamics of perceptual decision making." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7552.

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Models computacionals basats en xarxes a gran escala d'inspiració neurobiològica permeten descriure els correlats neurals de la decisió observats en certes àrees corticals com una transició entre atractors de la xarxa cortical. L'estimulació provoca un canvi en el paisatge d'atractors que afavoreix la transició entre l'atractor neutre inicial a un dels atractors associats a les eleccions categòriques. El soroll present en el sistema introdueix indeterminació en les transicions. En aquest treball mostrem l'existència de dos mecanismes de decisió qualitativament diferents, cadascun amb signature
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45

Vellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.

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In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron], Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden. Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert. IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pul
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46

Kourkoulas-Chondrorizos, Alexandros. "Online optimisation of information transmission in stochastic spiking neural systems." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5832.

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An Information Theoretic approach is used for studying the effect of noise on various spiking neural systems. Detailed statistical analyses of neural behaviour under the influence of stochasticity are carried out and their results related to other work and also biological neural networks. The neurocomputational capabilities of the neural systems under study are put on an absolute scale. This approach was also used in order to develop an optimisation framework. A proof-of-concept algorithm is designed, based on information theory and the coding fraction, which optimises noise through maximising
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47

Ncube, Israel. "Stochastic approximation of artificial neural network-type learning algorithms, a dynamical systems approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ60559.pdf.

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48

Curram, Stephen Paul. "Representing intelligent decision making in discrete event simulation : a stochastic neural network approach." Thesis, University of Warwick, 1997. http://wrap.warwick.ac.uk/59461/.

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The problem of representing decision making behaviour in discrete event simulation was investigated. Of particular interest was modelling variety in the decisions, where different people might make different decisions even where the same circumstances hold. An initial investigation of existing and alternative approaches for representing decision making was carried out. This led to the suggestion of using a neural network to represent the decision making behaviour in the form of a multi-criteria probability distribution based on data of observed decision making. The feasibility of the stochasti
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49

Glover, John Sigsworth. "The hardware implementation of an artificial neural network using stochastic pulse rate encoding principles." Thesis, Durham University, 1995. http://etheses.dur.ac.uk/5423/.

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In this thesis the development of a hardware artificial neuron device and artificial neural network using stochastic pulse rate encoding principles is considered. After a review of neural network architectures and algorithmic approaches suitable for hardware implementation, a critical review of hardware techniques which have been considered in analogue and digital systems is presented. New results are presented demonstrating the potential of two learning schemes which adapt by the use of a single reinforcement signal. The techniques for computation using stochastic pulse rate encoding are pres
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50

Jansa, Jakub. "Stochastická předpověď průměrných měsíčních průtoku ve vybraném vodoměrném profilu." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2016. http://www.nusl.cz/ntk/nusl-240072.

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The diploma thesis is focused on the average monthly flows forecast in the selected hydrometric profile. Aim of this work will be evaluation of the calculated values and the interpretation of the results in understandable form. The next step will be find an appropriate connection between randomly-generated inputs in the form of random real flow series using the standard hydrological prediction models. This models are based on the principles of artificial intelligence and probability model. The result of the work will be verification of procedures and compilation of mean monthly flow stochastic
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