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

作　　者：李红 吕恕 钟守铭 

机构地区：[1]School of Applied Mathematics, University of Electronic Science and Technology of China Chengdu 610054 China

出　　处：《Journal of Electronic Science and Technology of China》2005年第2期126-129,共4页中国电子科技（英文版）

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.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.

关 键 词：flow invariance DELAY different time-scales neural network asymptotic stability 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]