Anti-periodic Solutions for High-Order Neural Networks with Mixed Time Delays

被引：0

作者：

Chen, Xiaofeng ^{[1
]}

Song, Qiankun ^{[1
]}

机构：

[1] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT I | 2011年 / 6675卷

关键词：

Anti-periodic; Hight-order Neural networks; Exponential stability; Mixed delays; EVOLUTION-EQUATIONS; VARYING DELAYS; INTERPOLATION; EXISTENCE; STABILITY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the paper, the problem on the stability of anti-periodic solutions is investigated for high-order neural networks with discrete and distributed time delays. Several sufficient conditions for checking the existence, uniqueness and global exponential stability of anti-periodic solution for the considered neural networks are given. A numerical example is given to show its effectiveness.

引用

页码：260 / 269

页数：10

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