Research on Prediction of Nonlinear Regression Problem Based on PSO-SVM

The accurate prediction of nonlinear regression problems plays a central role in many practical applications, and the improvement of its prediction accuracy holds significant practical value that cannot be ignored. In this paper, we take the nonlinear regression prediction test dataset provided by the Github website as the research object, and adopts support vector machines for regression prediction. Based on the characteristics of the data, a regression prediction model based on improved SVM is constructed. Combining the excellent nonlinear mapping ability of SVM with the advantages of particle swarm optimization algorithm in the optimal selection of initial weights and thresholds, a PSO-SVM model is established for regression prediction of data. Experimental results show that, compared with traditional regression prediction models, the prediction results of the PSO-SVM model are superior, with a significant improvement in accuracy.