Stability and Synchronization Control of Stochastic Neural Networks

Wuneng Zhou, Ph. D., Professor, Doctoral Supervisor 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.

“Neural networks are important tools for solving problems in many fields of applied sciences. … The volume is equipped with many figures, numerical examples and numerical simulations. Moreover, each chapter contains several references. The book can be recommended to readers having good knowledge in the foundations of neural networks, dynamical control systems and stochastic analysis.” (Kurt Marti, zbMATH 1355.60007, 2017)

• 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.