A Fuzzy Rule Extraction Method for ANFIS Using CFCM and Fuzzy Equalization | Zendy

Myung-Geun Chun | Zendy; Keun-Chang Kwak | Zendy; Jeong-Woong Ryu | Zendy; Witold Pedrycz | Zendy

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A Fuzzy Rule Extraction Method for ANFIS Using CFCM and Fuzzy Equalization

Author(s) -

Myung-Geun Chun,

Keun-Chang Kwak,

Jeong-Woong Ryu,

Witold Pedrycz

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.p0355

Subject(s) - adaptive neuro fuzzy inference system , computer science , fuzzy logic , defuzzification , neuro fuzzy , fuzzy rule , fuzzy classification , data mining , artificial intelligence , fuzzy control system , fuzzy set operations , fuzzy inference system , fuzzy number , algorithm , fuzzy set

In this paper, an efficient fuzzy rule generation scheme for Adaptive Network-based Fuzzy Inference System (ANFIS) using the conditional fuzzy c-means (CFCM) and fuzzy equalization (FE) methods is proposed. Here, the CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data. And also the FE method is used to automatically construct the fuzzy membership functions for ANFIS. From this, we can systematically obtain a small size of fuzzy rules that shows satisfactory performance for the given problems. We applied the proposed method to the truck-backing control and Box-Jenkins modeling problems and obtained a better result than previous work.

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