Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays

被引：28

作者：

Ali, M. Syed ^{[1
]}

Balasubramaniam, P. ^{[1
]}

机构：

[1] Gandhigram Rural Univ, Dept Math, Gandhigram 624302, Tamil Nadu, India

来源：

CHAOS SOLITONS & FRACTALS | 2009年 / 42卷 / 04期

关键词：

ASSOCIATIVE MEMORY NETWORKS; ROBUST STABILITY; TRANSMISSION DELAYS; SYSTEMS;

D O I：

10.1016/j.chaos.2009.03.138

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain 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 LMI optimization algorithms to guarantee the exponential stability of uncertain 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：2191 / 2199

页数：9

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