| 改进SVR及其在铁路客运量预测中的应用 |
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| 引用本文： | 夏国恩,金炜东,张葛祥.改进SVR及其在铁路客运量预测中的应用[J].西南交通大学学报,2007,42(4):494-498. |
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| 作者姓名： | 夏国恩 金炜东 张葛祥 |
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| 作者单位： | 1. 广西财经学院工商管理系,南宁,530003
2. 西南交通大学电气工程学院,四川,成都,610031 |
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| 摘 要： | 为了提高铁路客运量现有预测方法的预测能力,用训练样本与测试样本间的马氏距离对惩罚因子进行加权,对传统的支持向量回归机(SVR)进行了改进,在此基础上提出了基于改进SVR的铁路客运量时间序列预测方法.以1980~1998年铁路客运量预测为例,对SVR方法和BP人工神经网络(BPANN)方法进行了比较,结果表明,SVR方法能获得更准确的预测结果.
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| 关 键 词： | 铁路客运量 支持向量回归机 人工神经网络 时间序列预测 |
| 文章编号： | 0258-2724（2007）04-0494-05 |
| 修稿时间： | 2006-01-21 |
Improved Support Vector Regression and Its Application to Prediction of Railway Passenger Traffic Volume |
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| XIA Guoen,JIN Weidong,ZHANG Gexiang.Improved Support Vector Regression and Its Application to Prediction of Railway Passenger Traffic Volume[J].Journal of Southwest Jiaotong University,2007,42(4):494-498. |
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| Authors: | XIA Guoen JIN Weidong ZHANG Gexiang |
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| Institution: | 1. Dept, of Business Management, Guangxi University of Finance and Economics, Nanning 530003, China; 2. School of Electrical Eng., Southwest Jiaotong University, Chengdu 610031, China |
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| Abstract: | To improve the prediction abilities of the present methods for railway passenger traffic volume,support vector regression(SVR)was improved by weighting penalty coefficients using Mahalanobis distance between training and testing samples,and a model for predicting the time serial of railway passenger traffic volume was set up based on the improved SVR.The prediction of railway passenger traffic volume from 1980 to 1998 shows that the proposed method can obtain a more accurate result than the BP artificial neural network. |
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| Keywords: | railway passenger traffic volume support vector regression artificial neural network time serial prediction |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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