New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay

被引：0
|
作者：
ZHANG HuaGuang & WANG ZhanShan School of Information Science and Engineering
机构：
来源：
Science China(Information Sciences) | 2009年 / 06期
基金：
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词：
recurrent neural networks; global asymptotic stability; linear matrix inequality (LMI); time-varying delay;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is concerned with the global asymptotic stability of a class of recurrent neural networks with interval time-varying delay. By constructing a suitable Lyapunov functional, a new criterion is established to ensure the global asymptotic stability of the concerned neural networks, which can be expressed in the form of linear matrix inequality and independent of the size of derivative of time varying delay. Two numerical examples show the effectiveness of the obtained results.
引用
收藏
页码：942 / 948
页数：7
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