Comparison of Defuzzification Techniques for Analysis of Non-interval Data
Namdar Mogharreban, Lisabeth Fisher DiLalla · 2006
Defuzzification plays an important role in the implementation of a fuzzy system since the crisp value generated best represents the possibility distribution of all possible fuzzy control outputs. The focus of this paper is on comparison of several defuzzification strategies in two fuzzy inference systems designed to analyze questionnaires. Two different questionnaires were analyzed, one having two fuzzy rules and one having three fuzzy rules for the inference component. The output of centroid, bisector, mean of maximum (MOM), and largest of maximum (LOM) defuzzification methods were compared with the output of a conventional statistical analysis. Significant correlation was found between the statistical outputs and the fuzzy inference outputs. It appears that with non-interval data, typical of the kind of data collected in social science studies, the choice of defuzzification method has no influence on the output. As is suggested in the literature, this may be due to the match between the properties of the various defuzzification methods and the application