Robust stability criteria for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks of neutral type

被引：27

作者：

Muralisankar, S. ^{[1
]}

Gopalakrishnan, N. ^{[1
]}

机构：

[1] Madurai Kamaraj Univ, Sch Math, Madurai 625021, Tamil Nadu, India

来源：

NEUROCOMPUTING | 2014年 / 144卷

关键词：

Cohen-Grossberg neural networks; Asymptotic stability; Takagi-Sugeno fuzzy model; Wirtinger-type inequality; Linear matrix inequality; DELAY-DEPENDENT STABILITY; TIME-VARYING DELAYS; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; GLOBAL STABILITY; MIXED DELAYS; SYSTEMS; DISCRETE;

D O I：

10.1016/j.neucom.2014.04.019

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of this paper is to analyze the robust stability problem of Takagi-Sugeno fuzzy Cohen-Grossberg neural networks of neutral type. By constructing a Lyapunov-Krasovskii functional, which contains some triple and quadruple integral terms, and using a vector Wirtinger-type inequality approach, a delay dependent criterion is obtained to guarantee the stability of the addressed system. These conditions are expressed in terms of linear matrix inequalities that can be easily facilitated by using some standard numerical packages. Finally, numerical examples are given to illustrate the strength of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：516 / 525

页数：10

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