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An RBF neural network based on improved black widow optimization algorithm for classification and regression problems
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-RB...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871759/ https://www.ncbi.nlm.nih.gov/pubmed/36703878 http://dx.doi.org/10.3389/fninf.2022.1103295 |
Sumario: | 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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