Optimization of interval type-2 fuzzy logic controllers with rule base size reduction using genetic algorithms

Abstract

This paper presents optimization technique to develop type-2 fuzzy systems (FSs) through hybrid genetic algorithms (HGAs). The proposed optimization technique works as follows: (i) Optimize the type-2 membership functions (ii) Learn the rule base through genetic algorithms (iii) Apply the reducing technique to reduce the rule base. (iv) Build the FSs based on type-2 membership functions and the reduced rule base. For concurrently works step (i) and (ii), we used real and binary coded coupled GAs for the optimization technique. Real coded GAs is used to tune the type-2 membership functions and binary coded GAs is used to learn and reducing the fuzzy rules. For intelligent control of a two degree freedom inverted pendulum system, the control algorithm is used. Finally, the simulation studies show that the generated controller performance is better and comparable to the existing methods under normal conditions.