New results on global exponential stability of competitive neural networks with different time scales and time-varying delays  

作　　者：崔宝同 陈君 楼旭阳 

机构地区：[1]Research Center of Control Science and Engineering, Jiangnan University

出　　处：《Chinese Physics B》2008年第5期1670-1677,共8页中国物理B（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant No 60674026);the Jiangsu Provincial Natural Science Foundation of China (Grant No BK2007016);Program for Innovative Research Team of Jiangnan University of China

摘　　要：This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.

关 键 词：competitive neural network different time scale global exponential stability DELAY 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]