(8)Improvement of the ANFIS-based wave predictor models by the Particle Swarm Optimization
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Morteza Zanganeh∗
Department of Civil Engineering, Faculty of Engineering, Golestan University, Golestan, Aliabad Katoul, Iran
Received 12 November 2018; received in revised form 7 September 2019; accepted 8 September 2019
Available online 26 November 2019
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.
© 2019 Shanghai Jiaotong University. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license.(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: FIS; PSO; Prediction of wave parameters; Lake Michigan.