Dynamics of high-order Hopfield neural networks with time delays

被引：23
|
作者：
Qiu, Jianlong [1 ,2 ]
机构：
[1] Linyi Normal Univ, Dept Math, Linyi 276005, Peoples R China
[2] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China
来源：
NEUROCOMPUTING | 2010年 / 73卷 / 4-6期
基金：
中国国家自然科学基金;
关键词：
Global exponential stability; Periodic solution; Linear matrix inequality; Lyapunov functional; GLOBAL EXPONENTIAL STABILITY; PERIODIC-SOLUTIONS; ASSOCIATIVE MEMORIES; ASYMPTOTIC STABILITY; BAM NETWORKS;
D O I：
10.1016/j.neucom.2009.10.018
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 and the existence of periodic solution are studied for a class of high-order Hopfield neural networks (HHNNs) with time delays by employing Lyapunov method and linear matrix inequality (LMI) technique. Several simple sufficient conditions are given ensuring global exponential stability and the existence of periodic solution of HHNNs. The obtained results improve some previous works and do not require the symmetry of weight matrix. Finally, two illustrative examples are given to demonstrate the effectiveness of the obtained results. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：820 / 826
页数：7
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