Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

被引:40
作者
Ali, M. Syed [1 ]
Balasubramaniam, P. [1 ]
机构
[1] Gandhigram Rural Univ, Dept Math, Gandhigram 624302, Tamil Nadu, India
关键词
global asymptotic stability; linear matrix inequality; Lyapunov functional; stochastic fuzzy BAM neural networks; time-varying delays;
D O I
10.1016/j.physleta.2008.05.067
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB. (c) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:5159 / 5166
页数:8
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