STOCHASTIC NEURAL NETWORKS

被引：0
作者：
WONG, E
机构：
来源：
ALGORITHMICA | 1991年 / 6卷 / 03期
关键词：
NEURAL NETWORK; SIMULATED ANNEALING; DIFFUSION;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The first purpose of this paper is to present a class of algorithms for finding the global minimum of a continuous-variable function defined on a hypercube. These algorithms, based on both diffusion processes and simulated annealing, are implementable as analog integrated circuits. Such circuits can be viewed as generalizations of neural networks of the Hopfield type, and are called "diffusion machines." Our second objective is to show that "learning" in these networks can be achieved by a set of three interconnected diffusion machines: one that learns, one to model the desired behavior, and one to compute the weight changes.
引用
收藏
页码：466 / 478
页数：13
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