Prediction of wheel tread wear volume based on least squares support vector machine optimized by coupled simulated annealing.

This paper proposed an improved coupled simulated annealing (CSA) algorithm to optimize the hyper-parameters of least squares support vector machine (LS-SVM) . First, the CSA algorithm handled multiple independent parallel simulated annealing (SA) optimization process, which improved the optimization efficiency for hyper-parameters of LS-SVM model. Second, the acceptance temperature controlled the variance of the acceptance temperature which reduced the influence of the CSA algorithm to initialization parameters. Finally, it established CSA LS-SVM regression model to predict wheel tread wear based on the field data. The simulation results show that the proposed CSA LS-SVM regression model can trade off the model fit versus the model complexity, and the proposed model is effective for the wheel tread wear prediction. [ABSTRACT FROM AUTHOR]