含混合时滞和脉冲的Cohen-Grossberg神经网络的全局指数稳定性(英文)  

Global Exponential Stability of Cohen-Grossberg Neural Networks with Mixed Delays and Impulses

作　　者：韩天勇[1] 

机构地区：[1]成都大学信息科学与技术学院,四川成都610106

出　　处：《应用数学》2009年第2期260-269,共10页Mathematica Applicata

基　　金：Supported by National Natural Science Foundation of China(10371083)

摘　　要：本文讨论了含混合时滞和脉冲的Cohen-Grossberg神经网络的稳定性.通过应用M矩阵理论和不等式技巧,得到了含混合时滞的Cohen-Grossberg神经网络平衡态的全局指数稳定性的充分条件.相比以前同类文献,本文减弱了部分条件,推广了部分结论,并在文末给出了两个示例.本文结论对于设计和应用神经网络有一定实用价值.In this paper,the Cohen-Grossberg neural networks with mixed delays and impulses are considered. Applying the idea of M-matrix theory and inequality technique, several new sufficient conditions are obtained to ensure global exponential stability of equilibrium point for impulsive Co- hen-Grossberg neural networks with mixed delays and impulses. These results generalize a few pre- vious known results and remove some restrictions on the neural network. Two examples are given to show the effectiveness of the obtained results.

关 键 词：全局指数稳定 COHEN-GROSSBERG神经网络 混合时滞 脉冲 M矩阵 

分 类 号：O175.21[理学—数学]