Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

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• 作者：Qian  ; Ma ; Shengyuan  ; Xu  ; ;   ; ^{syxu02@yahoo.com.cn} ; Yun  ; Zou[Author vitae]
• 关键词：Markovian jump neural networks ; Partly unknown transition probabilities ; Asymptotic stability ; Synchronization
• 刊名：Neurocomputing
• 出版年：2011
• 出版时间：October 2011
• 年：2011
• 卷：74
• 期：17
• 页码：3404-3411
• 全文大小：369 K

文摘

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.