Determining appropriate membership functions to simplify fuzzy induction

Article type: Research Article

Authors: Tzung-Pei Hong, | Shyue-Liang Wang,

Affiliations: Department of Information Management, I-Shou University, Kaohsiung, 84008, Taiwan, R.O.C. E-mail: [email protected], [email protected]; URL: http://www.nuk.edu.tw/tphong

Abstract: Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into fuzzy linguistic values and to make fuzzy reasoning work. Recently, fuzzy systems that automatically derive fuzzy if-then rules from numeric data have been developed. In this paper, we propose a new learning method to automatically derive membership functions and fuzzy if-then rules from a set of given training examples. This method adopts a different way in building initial membership functions, thus making the learning procedure simpler than that used in [10]. Experiments are also made to show the performance of the newly proposed learning algorithm.

Keywords: fuzzy machine learning, fuzzy decision rule, membership function, decision table

DOI: 10.3233/IDA-2000-4105

Journal: Intelligent Data Analysis, vol. 4, no. 1, pp. 51-66, 2000