Asymptotic Stability and Exponential Stability of Impulsive Delayed Hopfield Neural Networks

被引：7
|
作者：
Chen, Jing [1 ]
Li, Xiaodi [2 ,3 ]
Wang, Dequan [4 ]
机构：
[1] Shandong Univ, Dept Math, Jinan 250100, Peoples R China
[2] Shandong Normal Univ, Sch Math Sci, Jinan 250014, Peoples R China
[3] Res Ctr Logist Optimizat & Predict Engn Technol, Jinan 250014, Shandong, Peoples R China
[4] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China
来源：
ABSTRACT AND APPLIED ANALYSIS | 2013年
基金：
中国国家自然科学基金;
关键词：
DISTRIBUTED DELAYS; SYNCHRONIZATION;
D O I：
10.1155/2013/638496
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A criterion for the uniform asymptotic stability of the equilibrium point of impulsive delayed Hopfield neural networks is presented by using Lyapunov functions and linear matrix inequality approach. The criterion is a less restrictive version of a recent result. By means of constructing the extended impulsive Halanay inequality, we also analyze the exponential stability of impulsive delayed Hopfield neural networks. Some new sufficient conditions ensuring exponential stability of the equilibrium point of impulsive delayed Hopfield neural networks are obtained. An example showing the effectiveness of the present criterion is given.
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页数：10
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