Complete Delay-Decomposing Approach to Asymptotic Stability for Neural Networks With Time-Varying Delays

被引：157

作者：

Zeng, Hong-Bing ^{[1
,2
]}

He, Yong ^{[1
]}

Wu, Min ^{[1
]}

Zhang, Chang-Fan ^{[2
]}

机构：

[1] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China

[2] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412008, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Delay-dependent; neural networks; stability; time-varying delay; GLOBAL EXPONENTIAL STABILITY; DEPENDENT STABILITY; ROBUST STABILITY; NEUTRAL SYSTEMS; CRITERIA;

D O I：

10.1109/TNN.2011.2111383

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 of neural networks with time-varying delays. A novel Lyapunov-Krasovskii functional decomposing the delays in all integral terms is proposed. By exploiting all possible information and considering independent upper bounds of the delay derivative in various delay intervals, some new generalized delay-dependent stability criteria are established, which are different from the existing ones and improve upon previous results. Numerical examples are finally given to demonstrate the effectiveness and the merits of the proposed method.

引用

页码：806 / 812

页数：7

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