General criteria for asymptotic and exponential stabilities of neural network models with unbounded delays

被引：25

作者：

Faria, Teresa ^{[1
,2
]}

Oliveira, Jose J. ^{[3
,4
]}

机构：

[1] Univ Lisbon, Fac Ciencias, Dept Matemat, P-1749016 Lisbon, Portugal

[2] Univ Lisbon, Fac Ciencias, CMAF, P-1749016 Lisbon, Portugal

[3] Univ Minho, Escola Ciencias, Dept Matemat & Aplicacoes, P-4710057 Braga, Portugal

[4] Univ Minho, Escola Ciencias, CMAT, P-4710057 Braga, Portugal

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2011年 / 217卷 / 23期

关键词：

Cohen-Grossberg neural network; Infinite delay; Distributed delay; Global asymptotic stability; Global exponential stability; DISTRIBUTED DELAYS; DIFFERENTIAL-EQUATIONS; GLOBAL STABILITY; MEMORY; SYSTEMS;

D O I：

10.1016/j.amc.2011.04.049

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

For a family of differential equations with infinite delay, we give sufficient conditions for the global asymptotic, and global exponential stability of an equilibrium point. This family includes most of the delayed models of neural networks of Cohen-Grossberg type, with both bounded and unbounded distributed delay, for which general asymptotic and exponential stability criteria are derived. As illustrations, the results are applied to several concrete models studied in the literature, and a comparison of results is given. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：9646 / 9658

页数：13

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