含时滞和脉冲的双向联想记忆神经网络模型的全局鲁棒一致渐近稳定性分析  

Global Robust Uniformly Asymptotic Stability Analysis of Bidirectional Associative Memory Neural Networks with Time Delays and Impulses

作　　者：赵亮[1] 李树勇[1] 杜启凤[1] 张秀英[1] 

机构地区：[1]四川师范大学数学与软件科学学院,四川成都610066

出　　处：《四川师范大学学报（自然科学版）》2013年第2期177-184,共8页Journal of Sichuan Normal University（Natural Science）

基　　金：国家自然科学基金(11271270);四川省应用基础研究(2009JY0066);四川省教育厅自然科学重点基金(10ZA125);高等学科博士点学科专项科研基金(20105134110001)资助项目

摘　　要：讨论了一类含时滞和脉冲的双向联想记忆神经网络模型的鲁棒渐近稳定性.通过构造恰当的Lyapunov泛函和使用线性矩阵不等式技巧,获得了该模型全局鲁棒一致渐近稳定的充分条件.通过2个例子说明了结论的有效性.In this paper, the global robust uniformly asymptotic stability of the equilibrium point for a class of bidirectional associatire memory neural networks with time delays and impulses is studied. New delay independent sufficient conditions are obtained for the global robust uniformly asymptotic stability of bidirectional associative memory neural networks with time delays and impulses by employing suitable Lyapunov funetionals and linear matrix inequality approach. Two examples are given to demonstrate the applicability and effectiveness.

关 键 词：时滞 脉冲 双向联想记忆神经网络模型 全局鲁棒一致渐近稳定性 

分 类 号：O175.26[理学—数学]