随机中立型时滞神经网络的全局渐近稳定性判据  

Global Asymptotic Stability Criteria for Stochastic Neutral-Type Delayed Neural Networks

作　　者：刘国权[1] 杨先一[1] 柴毅[1] 付伟[1] 冯伟[2] 时光[1] 

机构地区：[1]重庆大学自动化学院,重庆400044 [2]重庆教育学院计算机系,重庆400067

出　　处：《上海交通大学学报》2011年第8期1151-1156,共6页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金(60974090);中央高校基本科研业务费科研专项基金(CDJXS11172237);重庆市自然科学基金(2009BB2378)资助项目

摘　　要：研究一类含有变时滞的随机中立型神经网络的全局渐近稳定性问题.通过构造一个新的Lyapunov-Krasovskii泛函和不等式技术,得到了基于线性矩阵不等式表示的与时滞大小有关的时滞稳定的充分判据.该判据考虑了一类含有分布式时滞和随机扰动项并且激活函数是常量(正数、负数和零)的中立型神经网络,与现有文献相比,其更具有一般性和较少保守性.最后,通过2个算例分析,验证了所得结果是有效的.The problem of global asymptotic stability analysis for a class of stochastic neutral-type neural networks with variable delays was investigated.By constructing an appropriate Lyapunov functional combined with inequality technique,sufficient stability criteria dependent on the size of the time delay were obtained in terms of linear matrix inequality（LMI）.A class of neutral-type networks considered in the paper consists of distributed delays,stochastic perturbation and activation functions which are assumed to be constants（positive,negative,and zero）,so these proposed criteria are universal and representative.The proposed stability results are more universal and less conservative than the existing ones.Finally,two numerical examples were given to demonstrate the effectiveness of the proposed results.

关 键 词：全局渐近稳定性 随机扰动 神经网络 线性矩阵不等式 变时滞 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]