Overview
- Introduces for the first time the stochastic disturbance driven by Levy process in modeling stochastic neural networks
- Applies the M-matrix method to analyze and synthesize the synchronization criteria for stochastic neural networks
- Extends the existing results of the adaptive synchronization criteria of stochastic neural networks by getting the exponential stability (in the pth moment) conditions of the general stochastic delay deferential equation and the general neutral-type stochastic delay deferential equation, respectively
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 35)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.
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Authors and Affiliations
About the authors
1978. 2-1982. 1, B. S., HuaZhong Normal University, Wuhan, Hubei Province
2002. 3-2005. 3, Ph. D., Zhejiang University, Hangzhou, Zhejiang Province
1982. 2-1995. 1, Assistant, Lecturer, Associate Professor, Yunyang Teachers’ College, Danjiangkou, Hubei Province
1995. 2-2000. 7, Associate Professor, Professor, Jingzhou Normal University, Jingzhou, Hubei Province
2000. 8-2006. 4, Professor, Zhejing Normal University, Jinhua, Zhejiang Province
2006. 5-Present, Professor, Doctoral Supervisor, Donghua University, Shanghai
Some Honors:
2013, The science and technology progress award of petrochemical industry automation industry, the first prize, No. 4.
2011, The young and middle-aged discipline leaders of Zhejiang Province.
1999, Young and middle-aged expert with outstanding contributions of Hubei Province
Research Interests
Neural networks
Complex networks
Wireless sensor networksRobust control
Selected projects charged by Wuneng Zhou
[01] National “863” Key Program of China (2008AA042902).
[02] National Natural Science Foundation of China (61075060).
[03] Innovation Program of Shanghai Municipal Education Commission (12zz064).
Selected publications
Wuneng Zhou, Qingyu Zhu, Peng Shi, Hongye Su, Jian’an Fang, and Liuwei Zhou, Adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching parameters, IEEE Transactions on Cybernetics, 2014, Dec. 44 (12): 2848-2860.
Wuneng Zhou, Dongbing Tong, Yan Gao, Chuan Ji, Hongye Su. Mode and delay-dependent adaptive exponential synchronization in pth moment for stochastic delayed neural networks with Markovian switching. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23 (4): 662-668.
Zhengguang Wu, Hongye Su, Jian Chu and Wuneng Zhou. Improved delay-dependent stability condition of discrete recurrent neural networks with time-varying delays. IEEE Transaction on Neural Networks, 2010, 21 (4): 692-697.
Bibliographic Information
Book Title: Stability and Synchronization Control of Stochastic Neural Networks
Authors: Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Series Title: Studies in Systems, Decision and Control
DOI: https://doi.org/10.1007/978-3-662-47833-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2016
Hardcover ISBN: 978-3-662-47832-5Published: 25 August 2015
Softcover ISBN: 978-3-662-51716-1Published: 23 August 2016
eBook ISBN: 978-3-662-47833-2Published: 13 August 2015
Series ISSN: 2198-4182
Series E-ISSN: 2198-4190
Edition Number: 1
Number of Pages: XVI, 357
Number of Illustrations: 2 b/w illustrations, 80 illustrations in colour
Topics: Control and Systems Theory, Mathematical Models of Cognitive Processes and Neural Networks, Computational Intelligence