CONVERGENCE OF A SELF-ORGANIZING STOCHASTIC NEURAL NETWORK

被引：11
|
作者：
FRANCOIS, O
DEMONGEOT, J
HERVE, T
机构：
来源：
NEURAL NETWORKS | 1992年 / 5卷 / 02期
关键词：
STOCHASTIC NEURAL PROCESS; HEBBS LEARNING RULE; SELF-ORGANIZATION; SEQUENTIAL; PARTIALLY PARALLEL; OR MASSIVELY PARALLEL DYNAMICS; STOCHASTIC APPROXIMATION;
D O I：
10.1016/S0893-6080(05)80025-5
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 focus on the convergence of a stochastic neural process. In this process, a "physiologically plausible" Hebb's learning rule gives rise to a self-organization phenomenon. Some preliminary results concern the asymptotic behaviour of the nework given that the update of neurons is either sequential, partially parallel, or massively parallel. We shall pay attention to the fact that Hebbian learning is closely linked to the underlying dynamics of the network. Thereafter, we shall give, within the mathematical framework of stochastic approximation, some conditions for convergence of the learning scheme.
引用
收藏
页码：277 / 282
页数：6
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