New global exponential stability conditions for inertial Cohen-Grossberg neural networks with time delays

被引：60

作者：

Yu, Shenghua ^{[1
]}

Zhang, Zhengqiu ^{[2
]}

Quan, Zhiyong ^{[2
]}

机构：

[1] Hunan Univ, Sch Econ & Trade, Changsha 410079, Hunan, Peoples R China

[2] Hunan Univ, Coll Math, Changsha 410082, Hunan, Peoples R China

来源：

NEUROCOMPUTING | 2015年 / 151卷

关键词：

Inertial Cohen-Grossberg neural networks; Homeomorphism; Lyapunov functional; LMI; Inequality technique; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; PERIODIC-SOLUTIONS;

D O I：

10.1016/j.neucom.2014.10.043

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, global exponential stability of inertial Cohen-Grossberg neural networks with time delays is investigated. By using Homeomorphism theorem and inequality technique, a LMI-based global exponential stability condition and inequality form global exponential stability condition are obtained for the above neural networks. In our result, the assumptions for the differentiability and monotonicity on the behaved functions in Ke and Miao (2013) [23] are removed. Thus our results are less conservative than those obtained in Ke and Miao (2013) [23]. Hence, we obtain new global exponential stability for this neural network. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：1446 / 1454

页数：9

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