Cohen-Grossberg神经网络的全局指数稳定性  

Global Exponential Stability of Cohen-Grossberg Neural Networks

作　　者：黄家琳[1,2] 饶若峰[1,2] 

机构地区：[1]成都师范学院数学系,成都611130 [2]宜宾学院数学研究所,四川宜宾644007

出　　处：《西南大学学报（自然科学版）》2016年第2期78-82,共5页Journal of Southwest University(Natural Science Edition)

基　　金：国家"973"计划基金项目(2010CB732501);四川省科技厅资助项目(2012JYZ010);四川省教育厅项目(14ZA0274;12ZB349;08ZB002)

摘　　要：运用压缩映像原理研究了Cohen-Grossberg神经网络全局指数稳定的平凡解的存在性.值得一提的是,这是第一次用压缩映像原理证明Cohen-Grossberg神经网络的稳定性,而以前相关文献多用Leray-Schauder不动点原理.最后给出一个数值实例有力地证明了所获稳定性判据的有效性.In this paper,the contraction mapping theorem is employed to prove the existence of globally exponentially stable trivial solution for Cohen-Grossberg neural networks（CGNNs）.It is worth mentioning that it is the first time to use the contraction mapping theorem to prove the stability for CGNNs while only the Leray-Schauder fixed point theorem was applied in previous related literature.An example is given to illustrate the effectiveness of the proposed method due to the large allowable variation range of time-delay.

关 键 词：压缩映像原理 COHEN-GROSSBERG神经网络 全局指数稳定 

分 类 号：O193[理学—数学]