Robust stability of uncertain fuzzy Cohen-Grossberg 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

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 07期

关键词：

Fuzzy Cohen-Grossberg BAM neural networks (FCGBAMNNs); Global asymptotic stability; Linear matrix inequality; Lyapunov functional; Time-varying delays; BIDIRECTIONAL ASSOCIATIVE MEMORIES; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; CONTROL-SYSTEMS; IDENTIFICATION; CONSTANT; DESIGN;

D O I：

10.1016/j.eswa.2009.02.058

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Cohen-Grossberg type bidirectional associative memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by using LMI optimization algorithms to guarantee the asymptotic stability of uncertain Cohen-Grossberg BAM neural networks with time varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：10583 / 10588

页数：6

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