Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen-Grossberg neural networks with mixed delays

被引：83

作者：

Kao, Yong-Gui ^{[1
,2
,4
]}

Guo, Ji-Feng ^{[3
]}

Wang, Chang-Hong ^{[1
]}

Sun, Xi-Qian ^{[4
]}

机构：

[1] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Harbin 150001, Peoples R China

[2] Qingdao Univ, Shandong Prov Key Lab Ind Control Technol, Qingdao 266071, Peoples R China

[3] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China

[4] Harbin Inst Technol, Sch Sci, Weihai 264209, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2012年 / 349卷 / 06期

基金：

中国博士后科学基金;

关键词：

H-INFINITY CONTROL; TIME-VARYING DELAY; ASYMPTOTIC STABILITY; GLOBAL STABILITY; STATE ESTIMATION; LINEAR-SYSTEMS; LMI APPROACH; DISCRETE; CRITERIA; INTERVAL;

D O I：

10.1016/j.jfranklin.2012.04.005

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 devoted to investigating the robust stochastic exponential stability for reaction-diffusion Cohen-Grossberg neural networks (RDCGNNs) with Markovian jumping parameters and mixed delays. The parameter uncertainties are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Some criteria for delay-dependent robust exponential stability of RDCGNNs with Markovian jumping parameters are established in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing Matlab LMI toolbox. Numerical examples are provided to demonstrate the efficiency of the proposed results. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：1972 / 1988

页数：17

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