Global exponential convergence of periodic neural networks with time-varying delays

被引：11
|
作者：
Wu, Ailong [1 ,2 ]
Zeng, Zhigang [1 ,2 ]
Zhang, Jine [3 ]
机构：
[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
[3] E China Jiaotong Univ, Sch Basic Sci, Nanchang 330013, Peoples R China
来源：
NEUROCOMPUTING | 2012年 / 78卷 / 01期
基金：
高等学校博士学科点专项科研基金;
关键词：
Periodic neural networks; Global exponential convergence; Mixed monotone operator; STABILITY;
D O I：
10.1016/j.neucom.2011.04.045
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 convergence of a general class of periodic neural networks with time-varying delays is investigated. Based on the theory of mixed monotone operator, a testable algebraic criteria for ascertaining global exponential convergence is derived. Furthermore, the rate of exponential convergence and bound of the networks are also estimated. Finally, a numerical example is given to show the effectiveness of the obtained results. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：149 / 154
页数：6
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