Stability analysis of Markovian jumping stochastic Cohen Grossberg neural networks with discrete and distributed time varying delays  

作　　者：M.Syed Ali 

机构地区：[1]Department of Mathematics, Thiruvalluvar University

出　　处：《Chinese Physics B》2014年第6期131-137,共7页中国物理B（英文版）

基　　金：supported by DST Project(Grant No.SR/FTP/MS-039/2011)

摘　　要：In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays （MJSCGNNs） is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen-Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples.In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays （MJSCGNNs） is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen-Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples.

关 键 词：Cohen-Grossberg neural networks global asymptotic stability linear matrix inequality Lyapunovfunctional time-varying delays 

分 类 号：O211.63[理学—概率论与数理统计]