Tuning ANFIS Using a Simplified Sparrow Search Algorithm

Abstract

The objective of this paper is to develop an enhanced metaheuristic algorithm to train ANFIS on solving nonlinear regression problems. Firstly, we improved the sparrow search algorithm and then proposed a simplified sparrow search algorithm (SSSA). Secondly, the SSSA was hence employed to train the parameters of an initial raw ANFIS structure for a nonlinear regression problem. In order to evaluate the performance of SSSA on the tuning of (Adaptive neuro fuzzy inference system) ANFIS, we use PSO, (gray wolf optimization) GWO and basic SSA for comparison. Finally, the results of simulations indicate that, training ANFIS by SSSA reduce the root of mean of squared error in a more effective pattern. Therefore, the proposed technique increases the way to solve nonlinear regression problems.