Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple discrete and distributed time-varying delays

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• 作者：M. Syed Ali^a ; ^{syedgru@rediffmail.com} ; P. Balasubramaniam ; ^a ; ^{pbalgri@rediffmail.com} ; ^{balugru@gmail.com}
• 关键词：Cellular neural networks ; Global asymptotic stability ; Linear matrix inequality ; Lyapunov functional ; Multiple discrete and distributed time-varying delays ; Stochastic systems ; T– ; S fuzzy model
• 刊名：Communications in Nonlinear Science and Numerical Simulation
• 出版年：2011
• 出版时间：July 2011
• 年：2011
• 卷：16
• 期：7
• 页码：2907-2916
• 全文大小：278 K

文摘

In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the stability analysis for stochastic cellular neural networks with multiple discrete and distributed time varying delays. A novel linear matrix inequality (LMI) based stability criterion is derived to guarantee the asymptotic stability of stochastic cellular neural networks with multiple discrete and distributed time varying delays which are represented by T–S fuzzy models. The derived delay-dependent stability conditions are based on free-weighting matrices method, Lyapunov stability theory and LMI technique. In fact, these techniques lead to generalized and less conservative stability condition that guarantee the wide stability region. The delay-dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay is removed. Our results can be specialized to several cases including those studied extensively in the literature. Finally, numerical examples are given to demonstrate the effectiveness and conservativeness of our results.