Improved integral inequalities for stability analysis of delayed neural networks

被引：8
|
作者：
Wang, Feng-Xian [1 ]
Liu, Xin-Ge [1 ]
Tang, Mei-Lan [1 ]
Hou, Mu-Zhou [1 ]
机构：
[1] Cent S Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
来源：
NEUROCOMPUTING | 2018年 / 273卷
关键词：
Neural network; Exponential stability; Integral inequality; Free-weighting matrix; TIME-VARYING DELAY; GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; SYSTEMS; FUNCTIONALS; CRITERIA;
D O I：
10.1016/j.neucom.2017.07.054
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is concerned with the exponential stability of delayed neural networks. Combined the Legendre polynomials with free-weighting matrices technique, an improved free-matrix-based single integral inequality is given, which includes the general single integral inequality and the free-matrix-based single integral inequality as special cases. Furthermore, a free-matrix-based double integral inequality which improves the existing results is derived. As applications of these novel free-matrix-based integral inequalities, several exponential stability criteria with less conservatism for the delayed neural networks are obtained. The effectiveness of our main results is illustrated by three numerical examples from the literatures. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：178 / 189
页数：12
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