Majority Algorithm: A Formation for Neural Networks with the Quantized Connection Weights

Cheol-Young PARK  Koji NAKAJIMA  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E83-A   No.6   pp.1059-1065
Publication Date: 2000/06/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section of Papers Selected from 1999 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC'99))
Category: 
Keyword: 
neural networks,  multi-layer,  limit cycles,  quantized interconnection,  parity problem,  

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Summary: 
In this paper, we propose the majority algorithm to choose the connection weights for the neural networks with quantized connection weights of 1 and 0. We also obtained the layered network to solve the parity problem with the input of arbitrary number N through an application of this algorithm. The network can be expected to have the same ability of generalization as the network trained with learning rules. This is because it is possible to decide the connection weights, regardless of the size of the training set. One can decide connection weights without learning according to our case study. Thus, we expect that the proposed algorithm may be applied for a real-time processing.