Exponential stability of uncertain stochastic fuzzy BAM neural networks with time-varying delays

被引：29

作者：

Ali, M. Syed ^{[1
]}

Balasubramaniam, P. ^{[1
]}

机构：

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

来源：

NEUROCOMPUTING | 2009年 / 72卷 / 4-6期

关键词：

Global exponential stability; Linear matrix inequality; Lyapunov functional; Stochastic fuzzy BAM neural networks; Time-varying delays; BIDIRECTIONAL ASSOCIATIVE MEMORY; PERIODIC OSCILLATORY SOLUTION; GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; SYSTEMS;

D O I：

10.1016/j.neucom.2008.09.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Among the various fuzzy models, the well-known Takagi-Sugeno (TS) fuzzy model is recognized as a popular and powerful tool in approximating a complex nonlinear system. TS model provides a fixed structure to some nonlinear systems and facilitates the analysis of the system. This paper concerns with the global exponential stability of uncertain stochastic bidirectional associative memory (BAM) neural networks with time-varying delays which are represented by the TS fuzzy models. The stability conditions are derived using Lyapunov-Krasovskii approach, in combination with the linear matrix inequality (LMI) techniques. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：1347 / 1354

页数：8

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