Open AccessPremise Parameter Optimization on Adaptive Network Based Fuzzy Inference System Using Modification Hybrid Particle Swarm Optimization and Genetic Algorithm
Author(s) -
Muchamad Kurniawan,
Nanik Suciati
Publication year - 2019
Publication title -
jurnal iptek
Language(s) - English
Resource type - Journals
ISSN - 2477-507X
DOI - 10.31284/j.iptek.2018.v22i2.274
Subject(s) - adaptive neuro fuzzy inference system , particle swarm optimization , gradient descent , artificial neural network , genetic algorithm , algorithm , estimator , mathematics , hybrid algorithm (constraint satisfaction) , mathematical optimization , artificial intelligence , computer science , fuzzy logic , fuzzy control system , statistics , constraint logic programming , stochastic programming , constraint programming
ANFIS is a combination of the Fuzzy Inference System (FIS) and Neural Network (NN), which has two training parameters, premise and consequent. In the traditional ANFIS, Least Square Estimator (LSE) and Gradient Descent (GD) are commonly used learning algorithms to train the two parameters. The combination of those two learning algorithms tends to produce the local optimal solution. Particle Swarm Optimization (PSO) can converge quickly but still allow for getting the local optimal solution because PSO is unable to find a new solution space. Meanwhile, Genetic Algorithm (GA) has been reported to be able to find a wider solution space. Hybrid PSOGA is expected to give a better solution. In this study, modification of hybrid PSOGA is used to train the premise parameter of ANFIS. In experiments, the accuracy of the proposed classification method, which is called ANFIS-PSOGA, is compared to ANFIS-GA and ANFIS-PSO on Iris flowers, Haberman, and Vertebral datasets. The experiment shows that ANFIS-PSOGA achieves the best result compared to the other methods, with an average of accuracy 99.85% on Iris flowers, 84.52% on Haberman, and 91.83% on Vertebral.