改进SVR及其在铁路客运量预测中的应用 - 江南娱乐网页版入口官网下载安装

改进SVR及其在铁路客运量预测中的应用

夏国恩,金炜东,张葛祥

文章导航> 江南娱乐网页版入口官网下载安装学报> 2007> 20(4): 494-498.

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]. 江南娱乐网页版入口官网下载安装学报, 2007, 20(4): 494-498.

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]. 江南娱乐网页版入口官网下载安装学报, 2007, 20(4): 494-498.

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, 20(4): 494-498.

Citation:

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, 20(4): 494-498.

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]. 江南娱乐网页版入口官网下载安装学报, 2007, 20(4): 494-498.

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]. 江南娱乐网页版入口官网下载安装学报, 2007, 20(4): 494-498.

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, 20(4): 494-498.

Citation:

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, 20(4): 494-498.

改进SVR及其在铁路客运量预测中的应用

详细信息

作者简介:
夏国恩(1977- ),男,博士研究生,研究方向为管理信息系统、决策支持系统,电话:13980432542,E-mail:gandlf007711@163.com;金炜东(1959- ),男,教授,电话:028-87603159
夏国恩(1977- ),男,博士研究生,研究方向为管理信息系统、决策支持系统,电话:13980432542,E-mail:gandlf007711@163.com;金炜东(1959- ),男,教授,电话:028-87603159

计量
- 文章访问数:1667
- HTML全文浏览量:74
- PDF下载量:426
- 被引次数:0
出版历程
- 收稿日期:2006-01-21
- 刊出日期:2007-08-25

Improved Support Vector Regression and Its Application to Prediction of Railway Passenger Traffic Volume

摘要

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.
- 铁路客运量/
- 支持向量回归机/
- 人工神经网络/
- 时间序列预测
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.
- railway passenger traffic volume/
- support vector regression/
- artificial neural network/
- time serial prediction

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文章访问数:1667
HTML全文浏览量:74
PDF下载量:426
被引次数:0

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