Global exponential stability of discrete-time Cohen-Grossberg neural networks

被引：100
|
作者：
Xiong, WJ [1 ]
Cao, JD [1 ]
机构：
[1] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China
来源：
NEUROCOMPUTING | 2005年 / 64卷
基金：
中国国家自然科学基金;
关键词：
discrete-time; Lyapunov functions; global exponential stability; Cohen-Grossberg neural networks;
D O I：
10.1016/j.neucom.2004.08.004
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Discrete-time versions of the continuous-time Cohen-Grossberg neural networks (CGNNs) are formulated and studied in this paper. Several sufficient conditions are obtained to ensure the global exponential stability of the discrete-time systems of CGNNs with and without delays based on Lyapunov methods. The obtained results have not assume the symmetry of the connection matrix, and monotonicity and the differentiability of the activation functions. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：433 / 446
页数：14
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