A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information | Zendy

Makoto Fujii | Zendy; Takeshi Furuhashi | Zendy

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A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information

Author(s) -

Makoto Fujii,

Takeshi Furuhashi

Publication year - 2000

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2000.p0024

Subject(s) - computer science , fuzzy rule , neuro fuzzy , artificial intelligence , fuzzy logic , classifier (uml) , fuzzy set operations , fuzzy classification , data mining , machine learning , defuzzification , fuzzy control system , fuzzy set , fuzzy number

This paper presents a new fuzzy classifier system (FCS) that can discover effective fuzzy rules efficiently. The system incorporates human knowledge in the form of symbolic information, and effectively limits its search space for fuzzy rules by using knowledge. The system also extracts symbolic information from acquired fuzzy rules for efficient exploration of other new fuzzy rules. Simulations are done to demonstrate the feasibility of the proposed method.

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