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Global Exponential Stability of High-Order Hopfield Neural Networks with Time Delays

被引：0
|
作者：
Qiu, Jianlong [1 ,2 ]
Cheng, Quanxin [3 ]
机构：
[1] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
[2] Linyi Normal Univ, Dept Math, Linyi 276005, Peoples R China
[3] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
来源：
SIXTH INTERNATIONAL SYMPOSIUM ON NEURAL NETWORKS (ISNN 2009) | 2009年 / 56卷
基金：
中国国家自然科学基金;
关键词：
ASYMPTOTIC STABILITY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the global exponential stability is studied for a class of high-order Hopfield neural networks (HHNNs) with time delays by employing Lyapunov method and linear matrix inequality (LMI) technique. Simple sufficient conditions are given ensuring global exponential stability of HHNNs. The proposed results improve some previous works and do not require the symmetry of weight matrix. In addition, the proposed conditions are easily checked by using the Matlab LMI Control Toolbox.
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页码：39 / +
页数：2
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