Parameter optimization of SVM algorithm for predicting physical parameters of SF6–Cu mixture plasma under local thermodynamic equilibrium

Abstract

The physical parameters of SF6–Cu mixture plasma are necessary for arc calculation simulation. The calculation of these parameters is very difficult, but the prediction of the corresponding parameters using the existing database is one of the methods used to solve this difficult problem. The support vector regression (SVR) algorithm can effectively deal with the high-dimensional input vector problem and is widely used in the prediction field. To address the problem that the RBF kernel parameters gamma and penalty coefficient C are difficult to be obtained using the SVR algorithm, which leads to some data not satisfying the prediction accuracy, this paper first uses particle swarm optimization and the gray wolf optimizer to optimize the parameters of SVR, then uses the optimized hyperparameters to predict the data, and finally compares and analyzes the predicted data before and after the optimization. The results show that the optimized SVR parameters obtained using the optimization-seeking algorithm can fit the data better, which verifies the feasibility of the optimization of SVR hyperparameters by the optimization-seeking algorithm.