Novel stability criteria for recurrent neural networks with time-varying delay

被引：67
作者：
Ji, Meng-Di [1 ,2 ]
He, Yong [1 ,2 ]
Zhang, Chuan-Ke [1 ,2 ]
Wu, Min [1 ,2 ]
机构：
[1] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China
[2] Hunan Engn Lab Adv Control & Intelligent Automat, Changsha 470083, Hunan, Peoples R China
来源：
NEUROCOMPUTING | 2014年 / 138卷
基金：
中国国家自然科学基金;
关键词：
Recurrent neural networks; Time-varying delay; Stability; Lyapunov-Krasovskii functional; GLOBAL ASYMPTOTIC STABILITY;
D O I：
10.1016/j.neucom.2014.01.024
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 stability analysis of recurrent neural networks with time-varying delay. An augmented Lyapunov-Krasovskii functional containing a triple integral term and considering more information of activation functions is constructed. Then, Wirtinger-based inequality and two zero-value free-weighting matrix equations are used to deal with the derivative of the Lyapunov-Krasovskii functional. Those treatments lead to less conservatism. A numerical example is given to verify the effectiveness and benefit of the proposed criteria. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：383 / 391
页数：9
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