Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays

被引：41
|
作者：
Yang, Zhichun [1 ]
Zhou, Weisong [2 ]
Huang, Tingwen [3 ]
机构：
[1] Chongqing Normal Univ, Dept Math, Key Lab Optimizat & Control, Minist Educ, Chongqing 400047, Peoples R China
[2] Chongqing Normal Univ, Dept Math, Chongqing 400047, Peoples R China
[3] Texas A&M Univ Qatar, Doha, Qatar
来源：
COGNITIVE NEURODYNAMICS | 2014年 / 8卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Exponential input-to-state stability (exp-ISS); Recurrent neural networks; Multiple time-varying delays; H-INFINITY; STABILIZATION;
D O I：
10.1007/s11571-013-9258-9
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov-Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.
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页码：47 / 54
页数：8
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