Global Uniform Asymptotic Stability of Competitive Neural Networks with Different-Time Scales and Delay

被引：1

作者：

李红

吕恕

钟守铭

机构：

[1] School of Applied Mathematics

[2] University of Electronic Science and Technology of China Chengdu China

来源：

JournalofElectronicScienceandTechnologyofChina | 2005年 / 02期

关键词：

flow invariance; delay; different time-scales neural network; asymptotic stability;

D O I：

暂无

中图分类号：

TP183 [人工神经网络与计算];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The global uniform asymptotic stability of competitive neural networks with different time scales and delay is investigated. By the method of variation of parameters and the method of inequality analysis, the condition for global uniformly asymptotically stable are given. A strict Lyapunov function for the flow of a competitive neural system with different time scales and delay is presented. Based on the function, the global uniform asymptotic stability of the equilibrium point can be proved.

引用

页码：126 / 129

页数：4