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
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...
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| Published in | 计算机应用研究 Vol. 32; no. 2; pp. 397 - 402 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
华东交通大学 电气与电子工程学院,南昌,330013
2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2015.02.018 |
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| Abstract | 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。 |
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| AbstractList | 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。 TP391.9; 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。 |
| Abstract_FL | 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. |
| Author | 衷路生 陈立勇 龚锦红 祝振敏 肖乾 |
| AuthorAffiliation | 华东交通大学电气与电子工程学院,南昌330013 |
| AuthorAffiliation_xml | – name: 华东交通大学 电气与电子工程学院,南昌,330013 |
| Author_FL | CHEN Li-yong ZHONG Lu-sheng ZHU Zhen-min GONG Jin-hong XIAO Qian |
| Author_FL_xml | – sequence: 1 fullname: ZHONG Lu-sheng – sequence: 2 fullname: CHEN Li-yong – sequence: 3 fullname: GONG Jin-hong – sequence: 4 fullname: ZHU Zhen-min – sequence: 5 fullname: XIAO Qian |
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| ClassificationCodes | TP391.9 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Prediction of wheel tread wear volume based on least squares support vector machine optimized by coupled simulated annealing |
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| EndPage | 402 |
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| Keywords | coupled simulated annealing tread wear 最小二乘支持向量机 踏面磨耗 耦合模拟退火 超参数优化 least squares support vector machine hyper-parameters optimization |
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| Notes | 51-1196/TP ZHONG Lu-sheng, CHEN Li-yong, GONG Jin-hong, ZHU Zhen-min, XIAO Qian （School of Electrical ＆ Electronic Engineering, East China Jiaotong University, Nanehang 330013, China） coupled simulated annealing; least squares support vector machine; hyper-parameters optimization; tread wear 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 |
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| PublicationTitle | 计算机应用研究 |
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| PublicationYear | 2015 |
| Publisher | 华东交通大学 电气与电子工程学院,南昌,330013 |
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| SubjectTerms | 最小二乘支持向量机 耦合模拟退火 超参数优化 踏面磨耗 |
| Title | 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 |
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