Generating Fuzzy Rules by Hybrid Method and Its Application to Classification Problems 


Vol. 4,  No. 5, pp. 1289-1296, May  1997
10.3745/KIPSTE.1997.4.5.1289


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  Abstract

To build up a knowledge-based system in an Artificial Intelligence System, selecting an appropriate set of rules is one of the key problems. In this paper, we discuss a new method for extracting fuzzy rules directly from fuzzy membership function data for pattern classification. The fuzzy rules with variable fuzzy regions are defined by sharing fuzzy space in fuzzy grid. These rules are extracted from membership function. Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using Ishibuchi.Finally, in order to demonstrate the effectiveness of the present method, simulation results are shown.

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[IEEE Style]

L. M. Rey and L. J. Pil, "Generating Fuzzy Rules by Hybrid Method and Its Application to Classification Problems," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 4, no. 5, pp. 1289-1296, 1997. DOI: 10.3745/KIPSTE.1997.4.5.1289.

[ACM Style]

Lee Mal Rey and Lee Jae Pil. 1997. Generating Fuzzy Rules by Hybrid Method and Its Application to Classification Problems. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 4, 5, (1997), 1289-1296. DOI: 10.3745/KIPSTE.1997.4.5.1289.

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