Stochastic stability of Markovian jumping Hopfield neural networks with constant and distributed delays

被引：32

作者：

Liu, Hongyang ^{[1
]}

Zhao, Lin ^{[1
]}

Zhang, Zexu ^{[2
]}

Ou, Yan ^{[1
]}

机构：

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

[2] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Heilongjiang Pr, Peoples R China

来源：

NEUROCOMPUTING | 2009年 / 72卷 / 16-18期

基金：

中国国家自然科学基金;

关键词：

Delay-dependence; Hopfield neural networks (HNNs); Markovian jump; Stochastic stability; GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; ROBUST STABILITY; LINEAR-SYSTEMS; DISCRETE; STABILIZATION; CRITERION;

D O I：

10.1016/j.neucom.2009.07.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the problem of stability analysis for Markovian jumping Hopfield neural networks (MJHNNs) with constant and distributed delays. Some new delay-dependent stochastic stability criteria are derived based on a novel Lyapunov-Krasovskii functional (LKF) approach. These new criteria based on the delay partitioning idea prove to be less conservative, since the conservatism could be notably reduced by thinning the delay partitioning. Numerical examples are provided to show the effectiveness and advantage of the proposed techniques. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：3669 / 3674

页数：6

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