Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters Using Adaptive Weighted PSO

Abstract

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from Genetic Algorithm (GA) method and using Adaptive Weighted for PSO. The simulation results show that in comparison with current gradient based training, the novel training can have a comparable adaptation to complex plants and train less parameter than gradient base methods. Also, the results show this new hybrid approach has less complexity than other gradient based methods.