Exponential convergence of Cohen-Grossberg neural networks with continuously distributed leakage delays

被引：0

作者：

Chen, Zhibin ^{[1
]}

Gong, Shuhua ^{[2
]}

机构：

[1] Hunan Univ Technol, Sch Sci, Zhuzhou 412000, Hunan, Peoples R China

[2] Jiaxing Univ, Coll Math Phys & Informat Engn, Jiaxing 314001, Zhejiang, Peoples R China

来源：

JOURNAL OF INEQUALITIES AND APPLICATIONS | 2014年

基金：

中国国家自然科学基金;

关键词：

Cohen-Grossberg neural network; global exponential convergence; continuously distributed delay; leakage term; TIME-VARYING DELAYS; PERIODIC-SOLUTIONS; STABILITY; BAM; TERMS; EXISTENCE; CRITERIA;

D O I：

10.1186/1029-242X-2014-48

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper is concerned with the global exponential convergence of Cohen-Grossberg neural networks with continuously distributed leakage delays. By using the Lyapunov functional method and differential inequality techniques, we propose a new approach to establishing some sufficient conditions ensuring that all solutions of the networks converge exponentially to the zero point. Our results complement some recent ones.

引用

页数：8

共 33 条

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