Asymptotic stationarity of discrete-time stochastic neural networks

被引：4
|
作者：
Wang, T
Sheng, ZH
机构：
[1] Southeast University, Nanjing
[2] Institute of Systems Engineering, School of Economics and Management, Southeast University
来源：
NEURAL NETWORKS | 1996年 / 9卷 / 06期
基金：
中国国家自然科学基金;
关键词：
neural networks; discrete-time stochastic systems; asymptotic stationarity; Lyapunov function; Markov chain; geometric ergodicity;
D O I：
10.1016/0893-6080(95)00131-X
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper deals with a class of synchronous discrete-time stochastic neural network models. Dynamic stabilities (or asymptotic stationarities) are analysed for two sub-classes of the models, i.e., a class of stochasticized models from Little, and a class of Hopfield-type stochastic models. Copyright (C) 1996 Elsevier Science Ltd.
引用
收藏
页码：957 / 963
页数：7
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