Global exponential stability of periodic solution for delayed complex-valued neural networks with impulses

被引：20
|
作者：
Xie, Dong [1 ,2 ]
Jiang, Yueping [1 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[2] Bozhou Univ, Dept Elect & Informat Engn, Bozhou 236800, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 207卷
关键词：
Complex-valued neural networks; Periodic solution; Exponential stability; Impulses; Delays; TIME-VARYING DELAYS; MU-STABILITY; MIXED DELAYS; EXISTENCE;
D O I：
10.1016/j.neucom.2016.04.054
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, a class of delayed complex-valued neural networks with impulses is investigated. By using Mawhin's continuation theorem of coincidence degree theory, a series of useful criteria on existence of periodic solution are established for the complex-valued neural networks. By constructing appropriate Lyapunov-Krasovskii functional, some sufficient conditions are derived for the global exponential stability of periodic solutions to the complex-valued neural networks. Finally, several examples with numerical simulations are given to highlight the effectiveness of our theoretical results via standard numerical software. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：528 / 538
页数：11
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