NEW RESULTS ON STABILITY OF STOCHASTIC NEURAL NETWORKS WITH MARKOVIAN SWITCHING AND MODE-DEPENDENT TIME-VARYING DELAYS

被引：1

作者：

Li, Peijuan ^{[1
,2
]}

Wang, Yueying ^{[3
]}

Zhou, Pingfang ^{[3
]}

Wang, Quanbao ^{[3
]}

Duan, Dengping ^{[3
]}

机构：

[1] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China

[2] Southeast Univ, Key Lab Microinertial Instrument & Adv Nav Techno, Minist Educ, Nanjing 210096, Jiangsu, Peoples R China

[3] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China

来源：

NEURAL NETWORK WORLD | 2014年 / 24卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Lyapunov-Krasovskii functional; Markovian switching; mode-dependent time delays; neural networks; stochastic systems; ROBUST EXPONENTIAL STABILITY; JUMPING PARAMETERS; ASYMPTOTIC STABILITY; NEUTRAL TYPE; MEAN-SQUARE; DISCRETE; CRITERIA; STABILIZATION;

D O I：

10.14311/NNW.2014.24.006

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 is concerned with the problem of exponential stability for a class of stochastic neural networks with Markovian switching and mode-dependent interval time-varying delays. A novel Lyapunov-Krasovskii functional is introduced with the idea of delay-partitioning, and a new exponential stability criterion is derived based on the new functional and free-weighting matrix method. This new criterion proves to be less conservative than the most existing results. Numerical examples are presented to illustrate the effectiveness of the proposed method.

引用

页码：103 / 116

页数：14

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