Membership Functions Generation Based on Density Function

Abstract

Fuzzy membership functions are considered as a key element in fuzzy systems. In order to generate a fuzzy membership function, there are two potential sources: expert knowledge and real data. However expert knowledge acquisition is a difficult issue, on the other hand using real data needs a methodology to translate real data to membership function. Most previous approaches considered membership function design highly dependent of fuzzy rule base and require the specification of membership functions¿ number. This paper attempts to overcome these problems and proposes an automatic membership function generation method. Our approach is based on a clustering technique and a density function for deriving cores of fuzzy sets. Experimental results show that our approach generates large core region which is more preferable than small core region in the context of membership function generation for neuro-fuzzy systems.