Delay-dependent stability analysis for continuous-time BAM neural networks with Markovian jumping parameters

被引：29

作者：

Liu, Hongyang ^{[1
]}

Ou, Yan ^{[1
]}

Hu, Jun ^{[1
]}

Liu, Tingting ^{[2
]}

机构：

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

[2] Harbin Inst Technol, Dept Automated Measurement & Control, Harbin 150001, Heilongjiang Pr, Peoples R China

来源：

NEURAL NETWORKS | 2010年 / 23卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Delay-dependence; BAM neural networks; Markovian jump; Linear matrix inequality (LMI); ROBUST EXPONENTIAL STABILITY; LINEAR-SYSTEMS; DISCRETE; STABILIZATION; CRITERION;

D O I：

10.1016/j.neunet.2009.12.001

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 bidirectional associative memory (BAM) neural networks with Markovian jumping parameters. 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. It is shown that the addressed stochastic BAM neural networks with Markovian jumping parameters are stochastically stable if three linear matrix inequalities (LMIs) are feasible. The feasibility of the LMIs can be readily checked by the Matlab LMI toolbox. A numerical example is provided to show the effectiveness and advantage of the proposed technique. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：315 / 321

页数：7

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