Improved delay-dependent stability analysis for uncertain stochastic neural networks with time-varying delay

被引：7
|
作者：
Liu, Fang [1 ]
Wu, Min [1 ]
He, Yong [1 ]
Yokoyama, Ryuichi [2 ]
机构：
[1] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China
[2] Waseda Univ, Grad Sch Environm & Energy Engn, Shinjuku Ku, Tokyo 1698555, Japan
来源：
NEURAL COMPUTING & APPLICATIONS | 2011年 / 20卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Uncertain stochastic neural networks; Time-varying delay; Ito's differential formula; Robust stability; Linear matrix inequality (LMI); GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; ASYMPTOTIC STABILITY; NEUTRAL TYPE; DISCRETE; CRITERIA; SYSTEMS;
D O I：
10.1007/s00521-010-0408-2
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 focuses on the problem of delay-dependent robust stability analysis for a class of uncertain stochastic neural networks with time-varying delay by employing improved free-weighting matrix method. Taking the relationship among the time-varying delay, its upper bound and their difference into account and using It's differential formula, some improved LMI-based delay-dependent stability criteria for stochastic neural networks are obtained without ignoring any terms, which guarantee systems globally robustly stochastically stable in the mean square. Finally, three numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method.
引用
收藏
页码：441 / 449
页数：9
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