Adaptive arrangement classifier via neuro-fuzzy modeling

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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
    Title of host publicationIEEE International Conference on Fuzzy Systems
    Place of PublicationPiscataway, NJ, United States
    PublisherIEEE
    Pages577-580
    Number of pages4
    Volume2
    Publication statusPublished - 2000
    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

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    Experiments

    ASJC Scopus subject areas

    • Chemical Health and Safety
    • Software
    • Safety, Risk, Reliability and Quality

    Cite this

    Shiina, K. (2000). Adaptive arrangement classifier via neuro-fuzzy modeling. In IEEE International Conference on Fuzzy Systems (Vol. 2, pp. 577-580). Piscataway, NJ, United States: IEEE.

    Adaptive arrangement classifier via neuro-fuzzy modeling. / Shiina, Kempei.

    IEEE International Conference on Fuzzy Systems. Vol. 2 Piscataway, NJ, United States : IEEE, 2000. p. 577-580.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Shiina, K 2000, Adaptive arrangement classifier via neuro-fuzzy modeling. in IEEE International Conference on Fuzzy Systems. vol. 2, IEEE, Piscataway, NJ, United States, pp. 577-580, FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems, San Antonio, TX, USA, 00/5/7.
    Shiina K. Adaptive arrangement classifier via neuro-fuzzy modeling. In IEEE International Conference on Fuzzy Systems. Vol. 2. Piscataway, NJ, United States: IEEE. 2000. p. 577-580
    Shiina, Kempei. / Adaptive arrangement classifier via neuro-fuzzy modeling. IEEE International Conference on Fuzzy Systems. Vol. 2 Piscataway, NJ, United States : IEEE, 2000. pp. 577-580
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    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.",
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