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
收藏
页数:8
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