Robust stability of uncertain stochastic fuzzy cellular neural networks

被引：25

作者：

Yang, Huizhong ^{[1
]}

Sheng, Li ^{[1
,2
]}

机构：

[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Jiangsu, Peoples R China

[2] Univ Maryland, Syst Res Inst, College Pk, MD 20742 USA

来源：

NEUROCOMPUTING | 2009年 / 73卷 / 1-3期

基金：

中国国家自然科学基金;

关键词：

Robust stability; Fuzzy cellular neural networks; Stochastic systems; Lyapunov functional; Linear matrix inequality; TIME-VARYING DELAYS; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; CRITERIA; SYSTEMS; DISCRETE;

D O I：

10.1016/j.neucom.2009.02.021

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Takagi-Sugeno (TS) fuzzy models are often used to describe complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear submodels. In this paper, the global robust stability problem of TS fuzzy cellular neural networks with parameter uncertainties and stochastic perturbations is investigated. Based on the Lyapunov method and stochastic analysis approaches, the globally robust asymptotically stable condition is presented in terms of linear matrix inequalities (LMIs), which can be easily solved by some standard numerical packages. A simulation example is provided to illustrate the effectiveness of the proposed criteria. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

引用

页码：133 / 138

页数：6

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