Analysis of dynamical behaviors for delayed neural networks with inverse Lipschitz neuron activations and impulses

被引：0
|
作者：
Wu, Huaiqin [1 ]
Sun, Jianzhi [1 ]
Zhong, Xiaozhu [1 ]
机构：
[1] Yanshan Univ, Coll Sci, Qinhuangdao 066004, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2008年 / 4卷 / 03期
关键词：
neural networks; global exponential stability; impulse; matrix inequality;
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 develops a novel class of neural networks with inverse Lipschitz neuron activations and impulses. Based on the topological degree theory and matrix inequality techniques, we study the existence and uniqueness of equilibrium point of the neural network. By constructing suitable Lyapunov functions, a sufficient condition ensuring global exponential stability of the neural network is presented. Finally, two numerical examples further illustrate the correctness of the results obtained in this paper.
引用
收藏
页码：705 / 715
页数：11
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