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
收藏
页码：103 / 116
页数：14
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