Details
Stability and Synchronization Control of Stochastic Neural Networks
Studies in Systems, Decision and Control, Band 35
|
CHF 118.00 |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 13.08.2015 |
| ISBN/EAN: | 9783662478332 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>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.</p>
Relative Mathematic Foundation.- Asymptotical and Exponential Stability and Synchronization for NN.- Robust Stability and Synchronization for NN.- Adaptive Stability and Synchronization for NN.- Stability and Synchronization for Neutral-type NN.- Stability and Synchronization for NN with Levy Noise.- Some Applications to Finance Based-on NN.
Wuneng Zhou, Ph. D., Professor, Doctoral Supervisor <br>1978. 2-1982. 1, B. S., HuaZhong Normal University, Wuhan, Hubei Province <br>2002. 3-2005. 3, Ph. D., Zhejiang University, Hangzhou, Zhejiang Province <br>1982. 2-1995. 1, Assistant, Lecturer, Associate Professor, Yunyang Teachers’ College, Danjiangkou, Hubei Province <br>1995. 2-2000. 7, Associate Professor, Professor, Jingzhou Normal University, Jingzhou, Hubei Province <br>2000. 8-2006. 4, Professor, Zhejing Normal University, Jinhua, Zhejiang Province <br>2006. 5-Present, Professor, Doctoral Supervisor, Donghua University, Shanghai <br>Some Honors: <br>2013, The science and technology progress award of petrochemical industry automation industry, the first prize, No. 4. <br>2011, The young and middle-aged discipline leaders of Zhejiang Province. <br>1999, Young and middle-aged expert with outstanding contributions of Hubei Province <br>Research Interests <br>Neural networks <br>Complex networks <br>Wireless sensor networks Robust control <br>Selected projects charged by Wuneng Zhou <br>[01] National “863” Key Program of China (2008AA042902). <br>[02] National Natural Science Foundation of China (61075060). <br>[03] Innovation Program of Shanghai Municipal Education Commission (12zz064). <br><br>Selected publications <br>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. <br>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. <br>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.
<p>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.</p>
<p>Introduces for the first time the stochastic disturbance driven by Levy process in modeling stochastic neural networks</p><p>Applies the M-matrix method to analyze and synthesize the synchronization criteria for stochastic neural networks</p><p>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</p><p>Includes supplementary material: sn.pub/extras</p>
Diese Produkte könnten Sie auch interessieren:
High-Frequency Oscillator Design for Integrated Transceivers
von: J. van der Tang, Dieter Kasperkovitz, Arthur H.M. van Roermund
CHF 177.00
