Global Exponential Stability of Delayed Neural Networks with Non-lipschitz Neuron Activations and Impulses

被引：0
|
作者：
Fu, Chaojin [1 ]
Wu, Ailong [1 ]
机构：
[1] Hubei Normal Univ, Dept Math, Huangshi 435002, Peoples R China
来源：
ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS | 2009年 / 5821卷
关键词：
Neural networks; Global exponential stability; Impulses; TIME-VARYING DELAYS; DISTRIBUTED DELAYS;
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：
This paper investigates global convergence for a novel class of delayed neural networks with non-Lipschitz neuron activations and impulses based on the topological degree theory and Lyapunov functional method. Some sufficient conditions are derived to ensure the existence, and global exponential stability of the equilibrium point, of neural networks. Finally, a numerical example is given to demonstrate the effectiveness of the obtained result.
引用
收藏
页码：92 / 100
页数：9
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