连续分布时滞Cohen-Grossberg神经网络渐近稳定性准则  

Criteria on asymptotical stability of Cohen-Grossberg neural networks with continuously distributed time-delay

作　　者：李涛[1] 费树岷[1] 王婷[2] 朱清[1] 

机构地区：[1]东南大学复杂工程系统测量与控制教育部重点实验室,江苏南京210096 [2]安庆师范学院数学与计算科学学院,安徽安庆246011

出　　处：《控制理论与应用》2009年第2期197-202,共6页Control Theory & Applications

基　　金：国家自然科学基金资助项目(60574006);高校博士点专项基金资助项目(20070286039).

摘　　要：通过选取一个新颖的Lyapunov-Krasovskii泛函和引入所考虑系统的等价描述系统,讨论了具有时变和连续分布时滞Cohen-Grossberg神经网络的渐近稳定性问题.在变时滞导函数有上界时,给出能判定系统是渐近稳定时滞相关的充分性条件.该条件以线性矩阵不等式形式给出,易于用MATLAB工具箱LMI进行检验.最后,数值例子说明了所得结论的有效性.Choosing a novel Lyapunov-Krasovskii functional and introducing the equivalent descriptor system, we investigate the asymptotical stability for the Cohen-Grossberg neural networks with time-varying and continuously distributed time-delays. When the derivative of the time-varying delay has an upper bound, a delay-dependent sufficient condition is obtained to guarantee the asymptotical stability of the above systems. The condition is presented in terms of LMIs, which can be checked easily by using LMI in MATLAB toolbox. Finally, numerical examples demonstrate the effectiveness of the proposed methods.

关 键 词：渐近稳定性 描述系统 Cohen—Grossberg神经网络 连续分布时滞 线性矩阵不等式(LMI) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]