A GA-Based Learning Algorithm for Binary Neural Networks

Masanori SHIMADA  Toshimichi SAITO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E85-A   No.11   pp.2544-2546
Publication Date: 2002/11/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Nonlinear Problems
binary neural networks,  supervised learning,  separating hyperplane,  genetic algorithm (GA),  

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This paper presents a flexible learning algorithm for the binary neural network that can realize a desired Boolean function. The algorithm determines hidden layer parameters using a genetic algorithm. It can reduce the number of hidden neurons and can suppress parameters dispersion. These advantages are verified by basic numerical experiments.