基于耦合模拟退火优化最小二乘支持向量机的车轮踏面磨耗量预测

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

针对最小二乘支持向量机(LS-SVM)超参数优化问题,提出采用改进耦合模拟退火(CSA)算法优化LSSVM超参数。首先,耦合模拟退火算法通过并行处理多个独立模拟退火(SA)寻优过程,提高LS-SVM模型超参数优化效率;然后通过调整接受温度控制耦合项超参数的接受概率方差,降低CSA算法初始设置对LS-SVM最优超参数确定过程稳健性的影响;最后结合既有线轮轨现场的实际检测数据,开展了基于改进耦合模拟退火优化的最小二乘支持向量机(CSA LS-SVM)回归模型性能对比实验。结果表明,CSA LS-SVM回归模型达到了模型精度、算法快速性、算法鲁棒性的有效折中,所建立的LS-SVM优化模型用于现场的车轮踏面磨耗量的预测是有效的。

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 controUed 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.