Data Driven Fuzzy Modeling for Sugeno and Mamdani Type Fuzzy Model using Memetic Algorithm

Abstract

The process of fuzzy modeling or fuzzy model identification is an arduous task. This paper presents the application of Memetic algorithms (MAs) for the identification of complete fuzzy model that includes membership function design for input and output variables and rulebase generation from the numerical data set. We have applied the algorithms on four bench mark data: A rapid Ni-Cd battery charger, the Box & Jenkinss gas-furnace data, the Iris data classification problem and the wine data classification problem. The comparison of obtained results from MAs with Genetic algorithms (GAs) brings out the remarkable efficiency of MAs. The result suggests that for these problems the proposed approach is better than those suggested in the literature.