Improvement of the ANFIS-based wave predictor models by the Particle Swarm Optimization

Abstract

In this paper, the Particle Swarm Optimization (PSO) algorithm is employed to deal with the Adaptive Network based Fuzzy Inference System (ANFIS) model drawbacks in prediction of wind –driven waves. In the ANFIS model selection of fuzzy IF-THEN rules structure and numbers is not an automatic process. In addition, in the ANFIS model extraction of fuzzy antecedent and consequent parameters is a gradient-based method which makes the answer susceptible to entrap in local optima. To cope with the ANFIS deficiencies, herein the PSO algorithm is coupled with the wave predictor FIS models in three viewpoints to optimize fuzzy subtractive clustering parameters, i.e. radii of clustering and quash factor, and the antecedent and consequent parameter of fuzzy IF-THEN rules. At first viewpoint, two PSO algorithms are used to optimize fuzzy subtractive clustering parameters and fuzzy IF-THEN rule parameters. In the second viewpoint, a PSO algorithm is used to optimize subtractive clustering parameters while the ANFIS model is used to tune the fuzzy IF-THEN rule parameters. In the third viewpoint, only a PSO algorithm is used to optimize the subtractive clustering parameters along with fuzzy IF-THEN rule parameters. Gathered data sets by National Data Buoy Center (NDBC) at Lake Michigan are used to evaluate the developed models for prediction of wave parameters including significant wave height and peak spectral period. Results indicate the efficiency of PSO algorithm to improve the ANFIS model accuracy.