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.
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页码：260 / 269
页数：10
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