Globally exponential stability and dissipativity for nonautonomous neural networks with mixed time-varying delays

被引：14
|
作者：
Jiang, Minghui [1 ]
Mu, Juan [1 ]
Huang, Dasong [1 ]
机构：
[1] China Three Gorges Univ, Inst Nonlinear Complex Syst, Yichang 443000, Hubei, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 205卷
基金：
中国国家自然科学基金;
关键词：
Existence; Dissipativity; Halanay inequality; Matrix measure; Stability; Nonautonomous neural networks; DEPENDENT STABILITY; DISTRIBUTED DELAYS; ASYMPTOTIC STABILITY; DISCRETE; SYSTEMS; PASSIVITY; CRITERIA; SYNCHRONIZATION; INEQUALITY; INTERVAL;
D O I：
10.1016/j.neucom.2016.04.025
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the problems of globally exponential stability, dissipativity and solutions' existence are investigated for nonautonomous neural networks with mixed time-varying delays as well as general activation functions. The mixed time-varying delays consist of both discrete and distributed delays. First, we give a Halanay inequality and combine matrix measure function inequality, sufficient conditions are established to ensure the dissipativity and globally exponential stability of the solutions of the considered neural networks in the end, then a criterion are obtained to guarantee the existence of the solutions of system. Finally, numerical examples are given to show the effectiveness of our theoretical results. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：421 / 429
页数：9
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