Learning method of parameters for fuzzy rules in universal learning network

Mitsuo Ikeuchi, Kotaro Hirasawa, Masanao Ohbayashi, Takayuki Furuzuki, Junichi Murata

Research output: Contribution to journalArticle

Abstract

In this paper, a new method which can alter the values of the parameters in neural networks is proposed in order to enhance the representation abilities of the networks. As an example, a fuzzy reference network is used to modify the parameters in this article, even though any kind of networks such as radial basis function networks and neural networks can be adopted to realize varying parameters. From simulations, it is shown that the network using the proposed method is better than the conventional neural networks in terms of representation abilities of the networks.

Original languageEnglish
Pages (from-to)225-231
Number of pages7
JournalResearch Reports on Information Science and Electrical Engineering of Kyushu University
Volume3
Issue number2
Publication statusPublished - 1998 Sep
Externally publishedYes

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Fuzzy rules
Neural networks
Radial basis function networks

ASJC Scopus subject areas

  • Hardware and Architecture
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

Cite this

Learning method of parameters for fuzzy rules in universal learning network. / Ikeuchi, Mitsuo; Hirasawa, Kotaro; Ohbayashi, Masanao; Furuzuki, Takayuki; Murata, Junichi.

In: Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol. 3, No. 2, 09.1998, p. 225-231.

Research output: Contribution to journalArticle

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