Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making

Abstract

Nowadays, decision making using fuzzy logic is a major research area for scientists, researchers and project managers. Construction of membership functions and fuzzy rules from numerical data is very important in various applications of the fuzzy set theory. Therefore, in this paper a model is proposed for development of membership functions and fuzzy rules from numerical data for decision making. The main advantage of the proposed model is its simplicity. The proposed model is applied on Fisher’s Iris data for decision making. The validation result shows that proposed model has a higher accuracy than existing models.