A Novel Neural Network Training Algorithm for the Identification of Nonlinear Static Systems: Artificial Bee Colony Algorithm Based on Effective Scout Bee Stage

Abstract

In this study, a neural network-based approach is proposed for the identification of nonlinear static systems. A variant called ABCES (ABC Based on Effective Scout Bee Stage) is introduced for neural network training. Two important changes are carried out with ABCES. The first is an update of “limit” control parameters. In ABC algorithm, “limit” value is fixed. It is adaptively adjusted according to number of iterations in ABCES. In this way, the efficiency of the scout bee stage is increased. Secondly, a new solution-generating mechanism for the scout bee stage is proposed. In ABC algorithm, new solutions are created randomly. It is aimed at developing previous solutions in the scout bee stage of ABCES. The performance of ABCES is analyzed on two different problem groups. First, its performance is evaluated on 13 numerical benchmark test problems. The results are compared with ABC, GA, PSO and DE. Next, the neural network is trained by ABCES to identify nonlinear static systems. 6 nonlinear static test problems are used. The performance of ABCES in neural network training is compared with ABC, PSO and HS. The results show that ABCES is generally effective in the identification of nonlinear static systems based on neural networks.