A Flexible Learning Algorithm for Binary Neural Networks

Atsushi YAMAMOTO  Toshimichi SAITO  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E81-A   No.9   pp.1925-1930
Publication Date: 1998/09/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
Keyword: 
binary neural networks,  boolean function,  supervised learning,  multi-layer perceptron,  function approximation,  

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Summary: 
This paper proposes a simple learning algorithm that can realize any boolean function using the three-layer binary neural networks. The algorithm has flexible learning functions. 1) moving "core" for the inputs separations,2) "don't care" settings of the separated inputs. The "don't care" inputs do not affect the successive separations. Performing numerical simulations on some typical examples, we have verified that our algorithm can give less number of hidden layer neurons than those by conventional ones.