Learning implementation on a deterministic general purpose neural network

被引：0
|
作者：
Lauria, Francesco E. [1 ]
机构：
[1] Univ di Napoli, Italy
来源：
Neural Networks | 1988年 / 1卷 / 1 SUPPL期
关键词：
Computer Programming--Algorithms;
D O I：
10.1016/0893-6080(88)90568-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The author discusses how to implement a Hebbian rule in an highly connected nonlinear and deterministic digital machine, taking due care to avoid unwanted interferences between new and previously stored programs. In particular, he discusses a self-organizing network called MAIA and an assembler-like algorithm that accepts the propositional calculus wffs as input and translates them into the coupling coefficients values, ie, the values associated with the connections between the network's nodes.
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