Exponential Stability of Anti-periodic Solution of Cohen-Grossberg Neural Networks with Mixed Delays

被引：0

作者：

Qin, Sitian ^{[1
]}

Tan, Yongyi ^{[1
]}

Wang, Fuqiang ^{[2
]}

机构：

[1] Harbin Inst Technol, Dept Math, Weihai 264209, Peoples R China

[2] Harbin Inst Technol, Sch Automobile Engn, Weihai 264209, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2016 | 2016年 / 9719卷

关键词：

Cohen-Grossberg neural networks; Anti-periodic solution; Exponential stability; Contraction mapping; TIME-VARYING DELAYS; PERIODIC-SOLUTION; EXISTENCE;

D O I：

10.1007/978-3-319-40663-3_19

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, we study the global exponential stability of anti-periodic solution of Cohen-Grossberg neural networks with mixed delays and distributed delays. Based on Lyapunov function and contraction mapping theorem, we introduce some sufficient conditions to ensure the existence and exponential stability of anti-periodic solution of Cohen-Grossberg neural networks. Finally, some numerical examples are provided to show the effectiveness of the obtained results.

引用

页码：160 / 167

页数：8

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