Global exponential stability analysis for a class of neural networks with time delays

被引：14
作者：
Guo, Yingxin [1 ,2 ]
Liu, Shu Tang [2 ]
机构：
[1] Qufu Normal Univ, Dept Math, Qufu 273165, Shandong, Peoples R China
[2] Shandong Univ, Coll Control Sci & Engn, Jinan 250061, Shandong, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2012年 / 22卷 / 13期
关键词：
neural networks with time delay; exponential stability; equilibrium; Brouwer's fixed point theorem; ASYMPTOTIC STABILITY; VARYING DELAYS; DISTRIBUTED DELAYS; ROBUST STABILITY; NONMONOTONIC NEURONS; ASSOCIATIVE MEMORY; PERIODIC-SOLUTIONS; NEUTRAL TYPE; DISCRETE; DYNAMICS;
D O I：
10.1002/rnc.1761
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Employing Brouwer's fixed point theorem, matrix theory, we made a further investigation of a class of neural networks with delays in this paper. A family of sufficient conditions were given for checking global exponential stability. These results have important leading significance in the design and applications of globally stable neural networks with delays. Our results extended and improved some earlier publications. Copyright (c) 2011 John Wiley & Sons, Ltd.
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收藏
页码：1484 / 1494
页数：11
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