Frontiers in Neuroinformatics (Jan 2023)

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

  • Hui Liu,
  • Hui Liu,
  • Guo Zhou,
  • Yongquan Zhou,
  • Yongquan Zhou,
  • Yongquan Zhou,
  • Huajuan Huang,
  • Huajuan Huang,
  • Xiuxi Wei,
  • Xiuxi Wei

DOI
https://doi.org/10.3389/fninf.2022.1103295
Journal volume & issue
Vol. 16

Abstract

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IntroductionRegression and classification are two of the most fundamental and significant areas of machine learning.MethodsIn this paper, a radial basis function neural network (RBFNN) based on an improved black widow optimization algorithm (IBWO) has been developed, which is called the 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.DiscussionSeveral classification and regression problems are utilized to verify the performance of the 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.ResultsCompared with other existing models, the experiments show that the proposed IBWO-RBF model achieves both accuracy and parsimony in various classification and regression problems.

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