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
收藏
页码：1891 / 1903
页数：13
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