A GA-based method for constructing TSK fuzzy rules from numerical data

Abstract

A method based on genetic algorithm (GA), a simple clustering procedure for rule base generation, and weighted least squares estimation is proposed to construct a Takagi-Sugeno-Kang (TSK) fuzzy inference system directly from numerical data. The rule-base generation method takes the approach of independently clustering input and output spaces, respectively, and assigning a weight to each rule to capture the relation in input-output data. Genetic process learns the number of linguistic terms per variable and the certainty factors of the rules (indirectly the membership-function parameters of the premise part of the fuzzy rules), and the weighted least squares method is used to determine the consequent part of the fuzzy rules. Simulation results on forecasting the stock market and a benchmark case study are included.