Adaptive arrangement classifier via neuro-fuzzy modeling

Kenpei Shiina

Adaptive arrangement classifier via neuro-fuzzy modeling

Kenpei Shiina^*

^*Corresponding author for this work

School of Education

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	577-580
Number of pages	4
Publication status	Published - 2000 Jan 1
Event	FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems - San Antonio, TX, USA Duration: 2000 May 7 → 2000 May 10

Other

Other	FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems
City	San Antonio, TX, USA
Period	00/5/7 → 00/5/10

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Cite this

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title = "Adaptive arrangement classifier via neuro-fuzzy modeling",

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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AB - 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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M3 - Paper

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T2 - FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems

Y2 - 7 May 2000 through 10 May 2000

ER -

Adaptive arrangement classifier via neuro-fuzzy modeling

Abstract

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ASJC Scopus subject areas

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