Learning Fuzzy Knowledge from Training Examples

Abstract

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 [5]. Experiments are also made to show the performance of the newly proposed learning algorithm.