Adaptive arrangement classifier via neuro-fuzzy modeling

Kenpei Shiina

Adaptive arrangement classifier via neuro-fuzzy modeling

Kenpei Shiina^*

^*この研究の対応する著者

教育学部

研究成果: Paper › 査読

抄録

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.

本文言語	English
ページ	577-580
ページ数	4
出版ステータス	Published - 2000 1 1
イベント	FUZZ-IEEE 2000: 9th IEEE International Conference on Fuzzy Systems - San Antonio, TX, USA 継続期間: 2000 5 7 → 2000 5 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

ソフトウェア
理論的コンピュータサイエンス
人工知能
応用数学

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引用スタイル

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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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Adaptive arrangement classifier via neuro-fuzzy modeling

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ASJC Scopus subject areas

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