AUTHOR=Liu Hui , Zhou Guo , Zhou Yongquan , Huang Huajuan , Wei Xiuxi 

TITLE=An RBF neural network based on improved black widow optimization algorithm for classification and regression problems

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 16 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.1103295

DOI=10.3389/fninf.2022.1103295

ISSN=1662-5196

ABSTRACT=In this paper, a radial basis function neural network (RBFNN) based on improved black widow optimization algorithm (IBWO) has been developed, is called IBWO-RBF model. In order to enhance the generalization ability of the IBWO-RBF neural network, the algorithm is designed with nonlinear time-varying inertia weight. Several classification and regression problems are utilized to verify the performance of IBWO-RBF model. In the first stage, the proposed model is applied to UCI dataset classification, nonlinear function approximation and nonlinear system identification; in the second stage, the model solves the practical problem of power load prediction. Compared with other existing models, the experiments show that the proposed IBWO-RBF model achieves both accuracy and parsimony in various classification and regression problems.