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

页码：957 / 963

页数：7

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