JACIII Vol.4 No.5 pp. 355-361
doi: 10.20965/jaciii.2000.p0355


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

Myung-Geun Chun*, Keun-Chang Kwak*, Jeong-Woong Ryu* and Witold Pedrycz**

*School of Electrical and Electronic Engineering Chungbuk National University Gaesin-Dong Cheong-Ju Chungbuk 361-763 KOREA

**Department of Electrical and Computer Engineering University of Alberta, Edmonton, Canada

August 18, 2000
October 1, 2000
September 20, 2000
Fuzzy rule extraction, Conditional FCM, Fuzzy modeling, ANFIS

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.

Cite this article as:
Myung-Geun Chun, Keun-Chang Kwak, Jeong-Woong Ryu, and Witold Pedrycz, “A Fuzzy Rule Extraction Method for ANFIS Using CFCM and Fuzzy Equalization,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.5, pp. 355-361, 2000.
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Last updated on Mar. 05, 2021