Training ANFIS by using the artificial bee colony algorithm

Abstract

In this study, a new adaptive network-based fuzzy inference system (ANFIS) training algorithm, the artificial bee colony (ABC) algorithm, is presented. Antecedent and conclusion parameters existing in the structure of ANFIS are optimized with the ABC algorithm and ANFIS training is realized. Identification of a set of nonlinear dynamic systems is performed in order to analyze the suggested training algorithm. The ABC algorithm is operated 30 times for each identification case and the average root mean square error (RMSE) value is obtained. Training RMSE values calculated for the four examples considered are 0.0325, 0.0215, 0.0174, and 0.0294, respectively. In addition, test error values for the same cases are respectively computed as 0.0270, 0.0186, 0.0167, and 0.0435. The results obtained are compared with those of known neuro-fuzzy-based methods frequently used in the literature in identification studies of nonlinear systems. It is shown that ANFIS can be trained successfully by using the ABC algorithm for the identification of nonlinear systems.