变时滞非线性细胞神经网络稳定性分析  

Stability analysis of nonlinear cellular neural networks with time-varying delay

作　　者：莫玉忠[1] 丁明智[2] 虞继敏[3] 

机构地区：[1]柳州师范高等专科学校数学与计算机科学系,广西柳州545004 [2]广西师范学院数学与计算机科学系,广西南宁530023 [3]重庆邮电大学自动化学院/教育部直属神经网络控制和智能仪器重点实验室,重庆400065

出　　处：《重庆邮电大学学报（自然科学版）》2010年第6期817-822,共6页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)

基　　金：重庆市自然科学基金(CSTC;2009BB3280);重庆市高等教育教学改革研究项目(09-3-086)~~

摘　　要：通过构造新的Lyapunov-Krasovskii泛函和线性矩阵不等式(linear matrix inequatity,LMI),研究变时滞非线性细胞神经网络渐近稳定性,利用牛顿-莱布尼兹公式,一些参数矩阵表达出系统变量之间的关系。从而得出一个具有变时滞相关的全局渐近稳定性判据,其扩展并改善了以前文献的结果。数值及仿真例子验证了结果的有效性。In this paper, a new Lyapunov-Krasovskii functional and the linear matrix inequatity（LMI） approach were proposed to deal with the problem of the global asymptotic stability of celluar neural networks with time-varying delay. Some parametermatrices were used to express the relationships among the system variables, and among the terms in Leibniz-New-ton formula. As a result, an elegant delay dependent stability for neural networks with time-varying delay was derived that is a generatlization of, and an improvement over, previous criterions. The numerical example and simulation example demonstrate the effectiveness of the condition.

关 键 词：全局渐近稳定性 线性矩阵不等式 Lyapunov—Krasovskii泛函 变时滞 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]