Majority algorithm: A formation for neural networks with the quantized connection weights

Cheol Young Park, Koji Nakajima

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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. \Ve 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 realtime processing.

Original languageEnglish
Pages (from-to)1059-1064
Number of pages6
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE83-A
Issue number6
Publication statusPublished - 2000 Jan 1

Keywords

  • Limit cycles
  • Multi-layer
  • Neural networks
  • Parity problem
  • Quantized interconnection

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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