具时滞细胞神经网络周期解的全局渐近稳定性  

Global asymptotic stability of periodic solutions of a class of delayed cellular neural networks

作　　者：常青[1] 周立群[1] 

机构地区：[1]天津师范大学数学科学学院,天津300387

出　　处：《天津师范大学学报（自然科学版）》2012年第4期22-26,31,共6页Journal of Tianjin Normal University：Natural Science Edition

基　　金：国家自然科学基金资助项目(60974144);天津师范大学博士基金资助项目(52LX34);天津高等学校科技发展资助项目(20100813)

摘　　要：讨论一类具时滞细胞神经网络周期解的全局渐近稳定性.通过构造合适的Lyapunov泛函及应用Barbalat引理,得到了具时滞细胞神经网络周期解的全局渐近稳定性的新的充分条件.最后通过数值算例及仿真证明了所得结论的有效性.The global asymptotic stability of periodic solutions of a class of delayed cellular neural networks is studied.In virtue of constructing a suitable Lyapunov functional and applying Barbalat lemma,new sufficient condition is derived for the global asymptotic stability of periodic solutions of the delayed cellular neural networks.And some examples and their simulations are given to illustrate the effectiveness of our methods.

关 键 词：时滞细胞神经网络 周期解 全局渐近稳定性 LYAPUNOV泛函 Barbalat引理 

分 类 号：O175.13[理学—数学] TP183[理学—基础数学]