Relevant bibliographies by topics / Stochastic adaptive networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stochastic adaptive networks'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Stochastic adaptive networks"

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Shafiei Dehkordi, Jinous, Andrea Conti, and Norman C. Beaulieu. "Adaptive Communications for Stochastic Networks." IEEE Transactions on Vehicular Technology 66, no. 9 (2017): 8263–75. http://dx.doi.org/10.1109/tvt.2017.2685940.

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Jiang, Xueling, Shuijie Qin, Dongbing Tong, and Liping Wang. "Adaptive Asymptotical Synchronization for Stochastic Complex Networks with Time-Delay and Markovian Switching." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/564058.

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The problem of adaptive asymptotical synchronization is discussed for the stochastic complex dynamical networks with time-delay and Markovian switching. By applying the stochastic analysis approach and theM-matrix method for stochastic complex networks, several sufficient conditions to ensure adaptive asymptotical synchronization for stochastic complex networks are derived. Through the adaptive feedback control techniques, some suitable parameters update laws are obtained. Simulation result is provided to substantiate the effectiveness and characteristics of the proposed approach.
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Pan, Yi Yong. "Adaptive Reliable Shortest Path in Discrete Stochastic Networks." Applied Mechanics and Materials 587-589 (July 2014): 1854–57. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.1854.

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This paper addresses adaptive reliable shortest path problem which aims to find adaptive en-route guidance to maximize the reliability of arriving on time in stochastic networks. Such routing policy helps travelers better plan their trips to prepare for the risk of running late in the face of stochastic travel times. In order to reflect the stochastic characteristic of travel times, a traffic network is modeled as a discrete stochastic network. Adaptive reliable shortest path problem is uniformly defined in a stochastic network. Bellman’s Principle that is the core of dynamic programming is sh
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Meng, Duo. "Adaptive Neural Networks Synchronization of a Four-Dimensional Energy Resource Stochastic System." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/863902.

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An adaptive neural networks chaos synchronization control method is proposed for a four-dimensional energy resource demand-supply system with input constraints. Assuming the response system contains unknown uncertain nonlinearities and unknown stochastic disturbances, the neural networks and robust terms are used to identify the nonlinearities and overcome the stochastic disturbances, respectively. Based on stochastic Lyapunov stability and robust adaptive theories, an adaptive neural networks synchronization control method is developed. In the design process, an auxiliary design system is emp
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Wang, Yu Ling, Yun Ling Luo, and Cheng Rong Xie. "Synchronization for Stochastic Complex Networks with Time Varying Delayed and No-Delayed Couping." Applied Mechanics and Materials 610 (August 2014): 270–73. http://dx.doi.org/10.4028/www.scientific.net/amm.610.270.

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In this paper, the problem of adaptive synchronization in pth moment is considered for stochastic complex networks with time varying delayed and no-delayed couping. By using the Lyapunov Krasovskii functional, stochastic analysis theory, sufficient condition to ensure adaptive synchronization in pth moment for stochastic time varying delayed complex networks is derived. To illustrate the effectiveness of the synchronization condition derived in this paper, a numerical example is provided finally.
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Xie, Chengrong, Yuhua Xu, and Dongbing Tong. "Mean Square Synchronization of Stochastic Nonlinear Delayed Coupled Complex Networks." Discrete Dynamics in Nature and Society 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/914140.

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We investigate the problem of adaptive mean square synchronization for nonlinear delayed coupled complex networks with stochastic perturbation. Based on the LaSalle invariance principle and the properties of the Weiner process, the controller and adaptive laws are designed to ensure achieving stochastic synchronization and topology identification of complex networks. Sufficient conditions are given to ensure the complex networks to be mean square synchronization. Furthermore, numerical simulations are also given to demonstrate the effectiveness of the proposed scheme.
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Zhou, Wuneng, Xueqing Yang, Jun Yang, Anding Dai, and Huashan Liu. "Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/479084.

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The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Mark
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Yang, Xinsong, and Jinde Cao. "Adaptive Pinning Synchronization of Complex Networks with Stochastic Perturbations." Discrete Dynamics in Nature and Society 2010 (2010): 1–21. http://dx.doi.org/10.1155/2010/416182.

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The adaptive pinning synchronization is investigated for complex networks with nondelayed and delayed couplings and vector-form stochastic perturbations. Two kinds of adaptive pinning controllers are designed. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are developed to guarantee the synchronization of the proposed complex networks even if partial states of the nodes are coupled. Furthermore, three examples with their numerical simulations are employed to show the effectiveness of the theoretical results.
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Fang, Yang, Kang Yan, and Kelin Li. "Robust Adaptive Exponential Synchronization of Stochastic Perturbed Chaotic Delayed Neural Networks with Parametric Uncertainties." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/963081.

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This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI) approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networ
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Gao, Song, Emma Frejinger, and Moshe Ben-Akiva. "Adaptive Route Choice Models in Stochastic Time-Dependent Networks." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (2008): 136–43. http://dx.doi.org/10.3141/2085-15.

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Adaptive route choice models are studied that explicitly capture travelers’ route choice adjustments according to information on realized network conditions in stochastic time-dependent networks. Two types of adaptive route choice models are explored: an adaptive path model in which a sequence of path choice models are applied at intermediate decision nodes and a routing policy choice model in which the alternatives correspond to routing policies rather than paths at the origin. A routing policy in this study is a decision rule that maps from all possible pairs (e.g., node, time) to the next l
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Dissertations / Theses on the topic "Stochastic adaptive networks"

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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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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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Gao, Song 1976. "Optimal adaptive routing and traffic assignment in stochastic time-dependent networks." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/30188.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.<br>Includes bibliographical references (p. 233-237).<br>A stochastic time-dependent (STD) network is defined by treating all link travel times at all time periods as random variables, with possible time-wise and link-wise stochastic dependency. A routing policy is a decision rule which specifies what node to take next out of the current node based on the current time and online information. A formal framework is established for optimal routing policy problems in STD networks, including g
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Teague, Kory Alan. "Approaches to Joint Base Station Selection and Adaptive Slicing in Virtualized Wireless Networks." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85966.

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Wireless network virtualization is a promising avenue of research for next-generation 5G cellular networks. This work investigates the problem of selecting base stations to construct virtual networks for a set of service providers, and adaptive slicing of the resources between the service providers to satisfy service provider demands. A two-stage stochastic optimization framework is introduced to solve this problem, and two methods are presented for approximating the stochastic model. The first method uses a sampling approach applied to the deterministic equivalent program of the stochastic
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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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Nesi, Luan Carlos. "Modelo hipermídia para geração de layouts de interfaces de aplicações." Universidade do Vale do Rio dos Sinos, 2014. http://www.repositorio.jesuita.org.br/handle/UNISINOS/3102.

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Submitted by Maicon Juliano Schmidt (maicons) on 2015-03-23T14:28:22Z No. of bitstreams: 1 Luan Carlos Nesi.pdf: 100100607 bytes, checksum: 6012e0f177d7b8f3807de72ff7d98315 (MD5)<br>Made available in DSpace on 2015-03-23T14:28:22Z (GMT). No. of bitstreams: 1 Luan Carlos Nesi.pdf: 100100607 bytes, checksum: 6012e0f177d7b8f3807de72ff7d98315 (MD5) Previous issue date: 2014-03-27<br>Milton Valente<br>Nesse trabalho foi desenvolvido um modelo computacional de Hipermídia Adaptativa para geração de layouts de interface de aplicações. A pesquisa partiu de uma revisão sobre Hipermídia Adaptativa,
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Manno, Gabriele Antonino. "Reliability modelling of complez systems: an adaptive transition system approach to match accuracy and efficiency." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1039.

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In questa tesi è introdotto un linguaggio di modellizzazione per la valutazione di misure di affidabilità di sistemi complessi. Adaptive Transition Systems (ATS) è un linguaggio di modellizzazione basato su macchine di stato interdipendenti dotate di variabili che sono inputs e outputs di un set di funzoni di comunicazione che permettono l'adattamento delle prprietà legate alle transizioni delle macchine di stato rispetto all'evoluzione del sistema. Le funzioni di comunicazione sono il mezzo per modellizzare le dipendenze tra le parti del sistema. Gli algoritmi per la risoluzione simulativa e
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Bol, Kieran G. "Redundant Input Cancellation by a Bursting Neural Network." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20061.

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One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Lepto
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Silva, Wilander Testone Pereira da. "Modelagem Estocástica: Teoria, Formulação e Aplicações do Algoritmo LMS." Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/301.

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Made available in DSpace on 2016-08-17T14:52:41Z (GMT). No. of bitstreams: 1 Dissertacao-WilanderTestonePereiraSilva.pdf: 3903191 bytes, checksum: b91ff906a27937df64d75b330c6ea137 (MD5) Previous issue date: 2016-03-11<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>In this dissertation we present a research in aspects of stochastic modeling, convergence and applications of least mean square (LMS) algorithm, normalized least mean square (NLMS) algorithm and proportionate normalized least mean square (PNLMS) algorithm. Specifically, the aim is to address the LMS algorithm i
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Rambha, Tarun. "Adaptive routing in schedule based stochastic time-dependent transit networks." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-08-6374.

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In this thesis, an adaptive transit routing (ATR) problem in a schedule based stochastic time-dependent transit network is defined and formulated as a finite horizon Markov Decision Process (MDP). The transit link travel times are assumed to be random with known probability distributions. Routing strategies are defined to be conditional on the arrival times at intermediate nodes, and the location and arrival times of other buses in the network. In other words, a traveler in the network decides to walk, wait or board a bus based on the real time information of all buses in the network. The obje
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Ding, Jing. "An Optimal Adaptive Routing Algorithm for Large-scale Stochastic Time-Dependent Networks." 2012. https://scholarworks.umass.edu/theses/902.

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The objective of the research is to study optimal routing policy (ORP) problems and to develop an optimal adaptive routing algorithm practical for large-scale Stochastic Time-Dependent (STD) real-life networks, where a traveler could revise the route choice based upon en route information. The routing problems studied can be viewed as counterparts of shortest path problems in deterministic networks. A routing policy is defined as a decision rule that specifies what node to take next at each decision node based on realized link travel times and the current time. The existing routing policy algo
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Books on the topic "Stochastic adaptive networks"

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Dong, Hongli, Zidong Wang, and Nan Hou. Networked Non-Linear Stochastic Time-varying Systems. Taylor & Francis Group, 2021.

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Networked Nonlinear Stochastic Time-Varying Systems: Analysis and Synthesis. CRC Press LLC, 2021.

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Dong, Hongli, Zidong Wang, and Nan Hou. Networked Non-Linear Stochastic Time-Varying Systems: Analysis and Synthesis. Taylor & Francis Group, 2021.

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Dong, Hongli, Zidong Wang, and Nan Hou. Networked Non-Linear Stochastic Time-Varying Systems: Analysis and Synthesis. Taylor & Francis Group, 2021.

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Networked Non-Linear Stochastic Time-Varying Systems: Analysis and Synthesis. Taylor & Francis Group, 2021.

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Book chapters on the topic "Stochastic adaptive networks"

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Tokic, Michel, and Günther Palm. "Adaptive Exploration Using Stochastic Neurons." In Artificial Neural Networks and Machine Learning – ICANN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_6.

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Barto, Andrew G., P. Anandan, and Charles W. Anderson. "Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata." In Adaptive and Learning Systems. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4757-1895-9_16.

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Grossi, Giuliano. "A Discrete Adaptive Stochastic Neural Model for Constrained Optimization." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_67.

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Yang, Jun, Junchao Ni, and Weisheng Chen. "Adaptive Output-Feedback Stochastic Nonlinear Stabilization Using Neural Network." In Advances in Neural Networks – ISNN 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_20.

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Tang, Tong Boon, Hsin Chen, and Alan F. Murray. "Adaptive Stochastic Classifier for Noisy pH-ISFET Measurements." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_76.

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Zhao, Birong, and Feiqi Deng. "Adaptive Exponential Synchronization of Stochastic Delay Neural Networks with Reaction-Diffusion." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01507-6_63.

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Li, Zifu, Tieshan Li, and Xiaori Gao. "Adaptive NN Dynamic Surface Control for Stochastic Nonlinear Strict-Feedback Systems." In Advances in Neural Networks – ISNN 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39068-5_14.

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Zheng, Cheng-De, Xixi Lv, and Zhanshan Wang. "Adaptive Synchronization for Stochastic Markovian Jump Neural Networks with Mode-Dependent Delays." In Advances in Neural Networks – ISNN 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39068-5_22.

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Zheng, Cheng-De, Chao-Ke Gong, and Zhanshan Wang. "Adaptive Stochastic Robust Convergence of Neutral-Type Neural Networks with Markovian Jump Parameters." In Advances in Neural Networks – ISNN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31346-2_40.

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Li, Zifu, and Tieshan Li. "Adaptive Neural Network Control for a Class of Stochastic Nonlinear Strict-Feedback Systems." In Advances in Neural Networks – ISNN 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25393-0_7.

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Conference papers on the topic "Stochastic adaptive networks"

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Tang, Shengjie, Xianxia Zhang, and Songbo Xie. "Adaptive Dynamic Planning Consensus Control of Multi Agent Systems Based on Stochastic Fuzzy Neural Networks." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662421.

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Cabanzo, Carlos, Paulo B. Lourenço, José C. Matos, and Mitsuyoshi Akiyama. "Seismic fragility curves of multi-span masonry arch bridges in Portugal." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0214.

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&lt;p&gt;Many masonry arch bridges (MABs) operate in several railway networks in several European countries, where MABs are the most common bridge typology. In Portugal, MABs represent around 30% of the total stock in the railway network. Their performance assessment is of utmost relevance to the management authorities. However, there is limited research regarding their seismic performance. Moreover, research has shown that the elevated computational cost associated with stochastic analyses is one of the main challenges for implementing stochastic-based assessments to larger sets of bridges. I
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Wang, Guangjie, Wentao Liu, Chengwei Bao, and Tong Zhao. "Stochastic Adaptive Neural Network Control for Hypersonic Vehicles." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065987.

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Guo, Xinyi, Qiong Liu, Shanshan Wang, and Li You. "Stochastic Geometry-Based MCS Adaption Analysis for Uplink Cellular Networks." In 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2025. https://doi.org/10.1109/wcnc61545.2025.10978118.

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Towfic, Zaid J., and Ali H. Sayed. "Adaptive stochastic convex optimization over networks." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736672.

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Chandramouli, R. "Model-free stochastic learning in adaptive wireless networks." In 2007 IEEE Sarnoff Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sarnof.2007.4567362.

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Gulcehre, Caglar, Jose Sotelo, Marcin Moczulski, and Yoshua Bengio. "A robust adaptive stochastic gradient method for deep learning." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965845.

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Ho, T. T., S. T. Ho, J. T. Bialasiewicz, and E. T. Wall. "Stochastic neural adaptive control using state space innovations model." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170740.

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Wei Song, Doug Edwards, Jose Luis Nunez-Yanez, and Sohini Dasgupta. "Adaptive stochastic routing in fault-tolerant on-chip networks." In 2009 3rd ACM/IEEE International Symposium on Networks-on-Chip. IEEE, 2009. http://dx.doi.org/10.1109/nocs.2009.5071442.

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Chen, Jinghui, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, and Quanquan Gu. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/452.

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Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method, which unifies the Adam/Amsgrad w
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Reports on the topic "Stochastic adaptive networks"

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Zhang, Junshan. Networked Information Gathering in Stochastic Sensor Networks: Compressive Sensing, Adaptive Network Coding and Robustness. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada590144.

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