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
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.