Self-association in multilayer linear networks with limited connectivity

被引：0
|
作者：
Palmieri, F [1 ]
Corvino, M [1 ]
机构：
[1] Univ Naples Federico II, Dipartimento Ingn Elettron, Naples, Italy
来源：
NEURAL NETWORKS | 1998年 / 11卷 / 04期
关键词：
Hebbian learning; linear neural networks; limited connectivity; principal components; self association;
D O I：
10.1016/S0893-6080(98)00037-9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We study the behavior of linear neural networks having constrained connectivity of Hebbian and anti-Hebbian synapses. We derive some general results for cascade architectures and a formula for the number of layers necessary to obtain a sufficiently close approximation to the principal components at the final outputs. Results of a number of simulations confirm the analyses. (C) 1998 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：601 / 609
页数：9
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