A Simple Class of Binary Neural Networks and Logical Synthesis

Yuta NAKAYAMA  Ryo ITO  Toshimichi SAITO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E94-A   No.9   pp.1856-1859
Publication Date: 2011/09/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E94.A.1856
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Nonlinear Problems
binary neural networks,  genetic algorithms,  logical synthesis,  

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This letter studies learning of the binary neural network and its relation to the logical synthesis. The network has the signum activation function and can approximate a desired Boolean function if parameters are selected suitably. In a parameter subspace the network is equivalent to the disjoint canonical form of the Boolean functions. Outside of the subspace, the network can have simpler structure than the canonical form where the simplicity is measured by the number of hidden neurons. In order to realize effective parameter setting, we present a learning algorithm based on the genetic algorithm. The algorithm uses the teacher signals as the initial kernel and tolerates a level of learning error. Performing basic numerical experiments, the algorithm efficiency is confirmed.