New algebraic conditions for global exponential stability of delayed recurrent neural networks

被引：22
|
作者：
Li, CD [1 ]
Liao, XF [1 ]
机构：
[1] Chongqing Univ, Coll Comp Sci & Engn, Chongqing 400030, 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：
global exponential stability; delayed recurrent neural networks; Lyapunov functional; bidirectional; associative memory neural networks;
D O I：
10.1016/j.neucom.2004.10.104
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The global exponential stability is further discussed for a class of delayed recurrent neural networks with Lipschitz-continuous activation functions. By constructing new Lyapunov functional and applying an elementary inequality technique, a set of new conditions with less restriction and less conservativeness are proposed for determining global exponential stability of the delayed neural network model with more general activation functions. The proposed results improve and generalize some previous reports in the literature. Several examples are also given to illustrate the validity and advantages of the new criteria. (c) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：319 / 333
页数：15
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