Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters as an Identifier Using Fuzzy PSO
Mohammad Teshnehlab, Mahdi Aliyari Shoorehdeli, Ali Khaki Sedigh · 2008
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS) and a new type of particle swarm optimizers (PSO). The previous works emphasized on gradient base method or least square (LS) based method. This study applied one of the swarm intelligent branches, PSO. The hybrid method composes fuzzy 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 fuzzy systems method and using fuzzy rules for tuning PSO parameters during training algorithms. The simulation results show that in comparison with current gradient based training, and authors previous hybrid method the proposed training have a good adaptation to complex plants and train less parameter than gradient base methods.