Parameter-dependent robust stability of uncertain neural networks with time-varying delay

被引：10

作者：

Lou, Xuyang ^{[1
,2
]}

Ye, Qian ^{[1
,2
]}

Cui, Baotong ^{[1
,2
]}

机构：

[1] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

[2] Jiangnan Univ, Sch IoT Engn, Wuxi 214122, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2012年 / 349卷 / 05期

基金：

中国国家自然科学基金;

关键词：

POLYTOPIC-TYPE UNCERTAINTIES; EXPONENTIAL STABILITY; LYAPUNOV FUNCTIONS; SYSTEMS;

D O I：

10.1016/j.jfranklin.2012.02.015

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is concerned with the problem of global robust asymptotic stability for delayed neural networks with polytopic parameter uncertainties and time-varying delay. A delay-dependent and parameter-dependent robust stability criterion for the equilibrium of delayed neural networks in the face of polytopic type uncertainties is presented by using a parameter-dependent Lyapunov functional and taking the relationship between the terms in the Leibniz-Newton formula into account. This criterion, expressed as a set of linear matrix inequalities, requires no matrix variable to be fixed for the entire uncertainty polytope, which produces a less conservative stability result. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1891 / 1903

页数：13

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