Exponential stability of a class of competitive neural networks with multi-proportional delays

被引：24

作者：

Zhou, Liqun ^{[1
]}

Zhao, Zhongying ^{[1
]}

机构：

[1] Tianjin Normal Univ, Sch Math Sci, Tianjin 300387, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2016年 / 44卷 / 03期

基金：

美国国家科学基金会;

关键词：

Competitive neural networks; Proportional delays; Exponential stability; Fixed point theorem; Delay differential inequality; DIFFERENT TIME SCALES; ASYMPTOTIC STABILITY; SYNCHRONIZATION; MULTISTABILITY;

D O I：

10.1007/s11063-015-9486-6

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 exponential stability of a class of competitive neural networks with multi-proportional delays is studied. First, through suitable transformations, a class of competitive neural networks with multi-proportional delays can be equivalently turned into a class of competitive neural networks with multi-constant delays and variable coefficients. By using fixed point theorem, the existence and uniqueness of equilibrium point of the system is proved. Furthermore by constructing appropriate delay differential inequality, two delay-independent and delay-independent sufficient conditions for the exponential stability of equilibrium point are obtained. Finally, several examples and their simulations are given to illustrate the effectiveness of the obtained results.

引用

页码：651 / 663

页数：13

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