Adaptive arrangement classifier via neuro-fuzzy modeling

Research output: Contribution to conferencePaper

Abstract

A hybrid fuzzy-neuro classifier that extracts rules in terms of polyhedrons in the input space is proposed. The network uses a fuzzy disjunctive normal form in its hidden layer to effectively map polyhedral regions, which are gradually adjusted during learning, to category labels. The major advantage of the present method lies in that it is quite simple in architecture, every layer enjoys a clear fuzzy logical interpretation, and the number of rules needed is very few. The results of classification experiments seem to be quite promising.

Original languageEnglish
Pages577-580
Number of pages4
Publication statusPublished - 2000 Jan 1
EventFUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems - San Antonio, TX, USA
Duration: 2000 May 72000 May 10

Other

OtherFUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems
CitySan Antonio, TX, USA
Period00/5/700/5/10

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Shiina, K. (2000). Adaptive arrangement classifier via neuro-fuzzy modeling. 577-580. Paper presented at FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems, San Antonio, TX, USA, .