Global exponential stability for a class of generalized neural networks with distributed delays

被引：35
|
作者：
Liao, XF [1 ]
Wong, KW
Li, CG
机构：
[1] Chongqing Univ, Dept Comp Sci & Engn, Chongqing 400044, Peoples R China
[2] Univ Elect Sci & Technol China, Coll Elect Engn, Chengdu 610054, Peoples R China
[3] City Univ Hong Kong, Dept Comp Engn & Informat Technol, Hong Kong, Hong Kong, Peoples R China
来源：
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2004年 / 5卷 / 03期
基金：
中国国家自然科学基金;
关键词：
generalized neural networks; distributed delays; Lyapunov functionals; global exponential stability; global asymptotic stability;
D O I：
10.1016/j.nonrwa.2003.12.002
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
By employing several Lyapunov functionals, the global exponential stability and asymptotic stability of generalized neural networks with distributed delays are investigated in this paper. A number of sufficient conditions for these types of stability are derived. They are milder and more general than previously known criteria, and can be applied to neural networks with constant connection weights as well as a broad range of activation functions assuming neither differentiability nor strict monotonicity. Furthermore, the results obtained for generalized neural networks with distributed delays do not assume symmetry of the connection matrix. (C) 2003 Published by Elsevier Ltd.
引用
收藏
页码：527 / 547
页数：21
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