| 基于LS-SVM的柴油机润滑油中磨粒含量预测 |
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| 引用本文： | 李霜,杨晓京,郭志伟.基于LS-SVM的柴油机润滑油中磨粒含量预测[J].润滑与密封,2009,34(2). |
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| 作者姓名： | 李霜 杨晓京 郭志伟 |
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| 作者单位： | 昆明理工大学现代农业工程学院,云南昆明,650224 |
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| 基金项目： | 昆明理工大学分析测试基金 |
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| 摘 要： | 介绍了最小二乘支持向量机(LS-SVM)回归算法的基本原理,并以490BPG型柴油机润滑油中磨损磨粒为研究对象,使用LS-SVM对磨粒的浓度数据进行了回归拟合并预测,并与基于人工神经网络的预测模型的预测结果进行了比较.结果表明,LS-SVM的预测模型的精确度较高,泛化能力强,是用于润滑油中磨粒浓度预测的一种有效的方法.
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| 关 键 词： | 最小二乘支持向量机 磨损磨粒 浓度预测 |
Forecasting of Wear Particle Concentration in Diesel Engine Lubricating Oil by Least Squares Support Vector Machine |
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| Li Shuang,Yang Xiaojing,Guo Zhiwei.Forecasting of Wear Particle Concentration in Diesel Engine Lubricating Oil by Least Squares Support Vector Machine[J].Lubrication Engineering,2009,34(2). |
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| Authors: | Li Shuang Yang Xiaojing Guo Zhiwei |
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| Affiliation: | Faculty of Modern Agricultural Engineering;Kunming University of Science and Technology;Kunming Yunnan 650224;China |
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| Abstract: | The basal principle of least squares support vector machine(LS-SVM)regression algorithm was introduced,and the wear particle concentration in 490BPG diesel engine lubricating oil was fitted by regression analysis and the wear tendency was predicted by LS-SVM.The predicted result was compared with that by artificial neural network(ANN)model.The result shows that the LS-SVM has better integrative performance and generalization ability and high precision,so it is an effective method for being used in forecasti... |
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| Keywords: | least squares support vector machine wear particle concentration prediction |
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