Exponential stability of hybrid stochastic neural networks with mixed time delays and nonlinearity

被引：20
作者：
Zhou, Wuneng [1 ,2 ]
Lu, Hongqian [1 ,4 ]
Duan, Chunmei [3 ]
机构：
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[2] Donghua Univ, Minist Educ, Engn Res Ctr Digitized Text & Fash Technol, Shanghai 201620, Peoples R China
[3] Shandong Normal Univ, Sch Management & Econ, Jinan 250014, Shandong, Peoples R China
[4] Shandong Inst Light Ind, Sch Elect Informat & Control Engn, Jinan 250353, Shandong, Peoples R China
来源：
NEUROCOMPUTING | 2009年 / 72卷 / 13-15期
关键词：
Neural networks; Uncertain systems; Stochastic systems; Mixed time-delays; Exponential stability; GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; STATE ESTIMATION; DISCRETE;
D O I：
10.1016/j.neucom.2009.04.012
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 robust exponential stability for a class of hybrid stochastic neural networks with mixed time-delays and Markovian jumping parameters. in this paper, free-weighting matrices are employed to express the relationship between the terms in the Leibniz-Newton formula. Based on the relationship, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions for the mixed time-delays neural networks with Markovian jumping parameters. Finally, two simulation examples are provided to demonstrate the effectiveness of the results developed. (C) 2009 Published by Elsevier B.V.
引用
收藏
页码：3357 / 3365
页数：9
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