Introduction: Regression and classification are two of the most fundamental and significant areas of machine learning.
Methods: In 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.
Discussion: Several 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.
Results: 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871759 | PMC |
| http://dx.doi.org/10.3389/fninf.2022.1103295 | DOI Listing |
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