Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays

被引：40
|
作者：
Chen, BS [1 ]
Wang, J
机构：
[1] Hubei Normal Univ, Dept Math, Hubei, Peoples R China
[2] Chinese Univ Hong Kong, Dept Automat & Comp Aided Engn, Shatin, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 06期
关键词：
global exponential periodicity; global exponential stability; mixed monotone operator; neural networks; oscillating connections; periodic oscillation; time-varying delay;
D O I：
10.1109/TNN.2005.857953
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, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.
引用
收藏
页码：1440 / 1448
页数：9
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