Robust exponential stability of Markovian jumping neural networks with mode-dependent delay

被引：27

作者：

Han, Wei ^{[1
]}

Liu, Yan ^{[2
]}

Wang, Linshan ^{[3
]}

机构：

[1] N Univ China, Dept Math, Taiyuan 030051, Shanxi, Peoples R China

[2] Hebei Normal Univ Sci & Technol, Dept Math & Phys, Qinhuangdao 066004, Hebei, Peoples R China

[3] Ocean Univ China, Dept Math, Qingdao 266071, Peoples R China

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2010年 / 15卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Markovian jumping neural networks; Robust exponential stability; Mode-dependent delay; Linear matrix inequality; TIME-VARYING DELAYS; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; GLOBAL STABILITY; CRITERION; PARAMETERS; DISCRETE;

D O I：

10.1016/j.cnsns.2009.09.024

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper deals with the robust exponential stability problem for a class of Markovian jumping neural networks with time delay. The delay considered varies randomly, depending on the mode of the networks. By using a new Lyapunov-Krasovskii functional, a delay-dependent stability criterion is presented, which can be expressed in terms of linear matrix inequalities (LMIs). A numerical example is given to show the effectiveness of the results. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：2529 / 2535

页数：7

共 24 条

[1]

[Anonymous], 1998, REAL TIME CONTROL SY

[2] Global robust stability analysis of neural networks with discrete time delays [J].

Arik, S .

CHAOS SOLITONS & FRACTALS, 2005, 26 (05) :1407-1414

[3] Stability of stochastic delay neural networks [J].

Blythe, S ;

Mao, XR ;

Liao, XX .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2001, 338 (04) :481-495

[4]

Boyd S., 1994, LINEAR MATRIX INEQUA

[5] Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays [J].

Cao, Jinde ;

Yuan, Kun ;

Li, Han-Xiong .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (06) :1646-1651

[6] An improved global asymptotic stability criterion for delayed cellular neural networks [J].

He, Y ;

Wu, M ;

She, JH .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (01) :250-252

[7] New delay-dependent stability criteria for neural networks with time-varying delay [J].

He, Yong ;

Liu, Guoping ;

Rees, D. .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2007, 18 (01) :310-314

[8] A based-on LMI stability criterion for delayed recurrent neural networks [J].

Liang, JL ;

Cao, JD .

CHAOS SOLITONS & FRACTALS, 2006, 28 (01) :154-160

[9] Delay-dependent stochastic stability of delayed Hopfield neural networks with Markovian jump parameters [J].

Lou, Xuyang ;

Cui, Baotong .

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 2007, 328 (01) :316-326

[10] New LMI conditions for delay-dependent asymptotic stability of delayed Hopfield neural networks [J].

Lou, Xuyang ;

Cui, Baotong .

NEUROCOMPUTING, 2006, 69 (16-18) :2374-2378

← 1 2 3 →