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An RBF neural network based on improved black widow optimization algorithm for classification and regression problems
Hui Liu1,2, Guo Zhou3, Yongquan Zhou1,2,4
1College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
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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