DELAY-DEPENDENT STABILITY FOR UNCERTAIN STOCHASTIC BAM NEURAL NETWORKS WITH TIME-VARYING DELAY

被引：0
|
作者：
Ji, Yan [1 ]
Cui, Baotong [1 ]
Qiu, Fang [2 ]
机构：
[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Jiangsu, Peoples R China
[2] Binzhou Univ, Dept Math & Informat Sci, Binzhou 256603, Shandong, Peoples R China
来源：
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2010年 / 35卷 / 1D期
基金：
中国国家自然科学基金;
关键词：
Bidirectional associative memory; neural networks; time-varying delay; linear matrix inequality; Lyapunov-Krasovskii functional; stochastic system; uncertain system; GLOBAL EXPONENTIAL STABILITY; ROBUST ASYMPTOTIC STABILITY; CRITERION;
D O I：
暂无
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This paper deals with the problem of delay-dependent asymptotically stability for stochastic bidirectional associative memory neural networks with time-varying structured uncertainties and time-varying delays. The parameter uncertainties are assumed to be norm bounded. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, new delay-dependent stability criteria for the asymptotically stability of stochastic bidirectional associative memory neural networks are derived in terms of linear matrix inequalities. Finally, a numerical example suggests that the proposed criteria are effective and are an improvement over previous ones.
引用
收藏
页码：65 / 77
页数：13
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