Dynamic Optimal ANFIS Parameters Tuning with Particle Swarm Optimization

Abstract

This paper presents dynamic modification parameters of the Adaptive Neuro-Fuzzy Inference System (ANFIS) using the Particle Swarm Optimization (PSO) algorithm. In the proposed ANFIS_PSO, each particle dynamically adjusts its weight to the optimal states of the particles using a nonlinear fuzzy model. Tests of the model were performed using the "Signal-Time Series". The methods are tested simultaneously until the best method to solve the problem is found. The proposed model takes advantage of PSO to tune ANFIS parameters by minimizing mean square error (MSE), root mean square error (RMSE), R-Squared (R2) and Mean Absolute Error (MEA) metrics. The main contribution is a strategy for dynamically finding the best result, which identifies methods for solving a given problem using different performance metrics depending on the problem. The proposed structure's results were compared with several machine learning algorithms. Simulation results show the effectiveness of the proposed algorithm.