一类变时滞离散Cohen-Grossberg神经网络模型周期解的存在性及其指数稳定性  

Existence and Stability of Periodic Solutions in a Discrete-Time Cohen-Grossberg Neural Network with Variable Delay

作　　者：王军平[1] 阮炯[1] 

机构地区：[1]复旦大学数学科学学院

出　　处：《数学年刊（A辑）》2007年第6期739-750,共12页Chinese Annals of Mathematics

基　　金：国家自然科学基金(No.70271065)资助的项目.

摘　　要：利用重合度理论研究了一类变时滞的离散Cohen-Grossberg神经网络模型的周期解,并得到了模型周期解的全局指数稳定性的充分条件,推广了已有的结果,为神经网络的应用提供了重要的理论基础.最后给出一个例子进行数值模拟,数值模拟的结果更好地验证了结论.This paper studies a discrete-time Cohen-Grossberg neural network with variable delay. Existence and global exponential stability of periodic solutions are investigated by using the continuation theorem of coincidence degree theory. Furthermore, sufficient conditions are given to guarantee the existence of w-periodic solution and all other solutions are convergent to it globally exponentially. At last, one example is given to show the effectiveness of results.

关 键 词：离散Cohen-Grossberg神经网络 重合度理论 周期解 全局指数稳定性 

分 类 号：O175[理学—数学]