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

摘要

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 an-nealing (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.%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LS-SVM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。