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JACIII Vol.4 No.1 pp. 24-30
doi: 10.20965/jaciii.2000.p0024
(2000)

Paper:

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A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information

Makoto Fujii and Takeshi Furuhashi

Department of Information Electronics, Graduate School of Eng., Nagoya University Furo-cho, Chikusa-ku, Nagoya 464-01, Japan

Received:
October 7, 1998
Accepted:
May 17, 1999
Published:
January 20, 2000
Creative Commons License
Keywords:
Rule discovery, Fuzzy classifier system, Genetic algorithm
Abstract

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.

Cite this article as:
M. Fujii and T. Furuhashi, “A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.1, pp. 24-30, 2000.
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Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

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