[1]张兵,邓卫.经济圈交通网络SVM评价方法[J].东南大学学报(自然科学版),2012,42(6):1227-1232.[doi:10.3969/j.issn.1001-0505.2012.06.037]
 Zhang Bing,Deng Wei.SVM evaluation method of transportation network in economic circle[J].Journal of Southeast University (Natural Science Edition),2012,42(6):1227-1232.[doi:10.3969/j.issn.1001-0505.2012.06.037]
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经济圈交通网络SVM评价方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第6期
页码:
1227-1232
栏目:
交通运输工程
出版日期:
2012-11-20

文章信息/Info

Title:
SVM evaluation method of transportation network in economic circle
作者:
张兵1 邓卫2
1 华东交通大学土木建筑学院, 南昌 330013; 2 东南大学交通学院, 南京 210096
Author(s):
Zhang Bing1 Deng Wei2
1 College of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013, China
2 School of Transportation, Southeast University, Nanjing 210096, China
关键词:
交通网络 经济圈 评价 支持向量机 神经网络
Keywords:
transportation network economic circle evaluation support vector machine(SVM) neural network
分类号:
U491
DOI:
10.3969/j.issn.1001-0505.2012.06.037
摘要:
为了快速高效、科学合理地对经济圈交通网络发展水平进行评价,建立了一种新的机器评价方法——支持向量机(SVM)综合评价方法.在对支持向量机理论研究分析的基础上,应用支持向量机多分类方法建立综合评价模型,提出了基于支持向量机理论的经济圈交通网络机器评价步骤,运用Matlab 7.0软件编程实现,并在综合评价程序中选用RBF核函数.最后,分别应用支持向量机综合评价方法、神经网络综合评价方法和物元评价方法对珠三角和长三角经济圈交通网络发展水平进行评价.结果表明,当选取合适的核函数以及相应的惩罚参数时,支持向量机评价方法在多模式识别及小样本数据分类上具有明显效果,比基于BP神经网络的评价方法效率更高、更准确.因此,该评价方法能够高效地评价经济圈交通网络发展水平.
Abstract:
To evaluate the development level of the transportation network in economic circle efficiently and scientifically, a new machine evaluation method called support vector machine comprehensive evaluation method is established. The comprehensive evaluation model is built by the support vector machine multi-classification method based on the analysis of the support vector machine theory, and the steps of the evaluation of the transportation network in economic circle are proposed. Then, comprehensive evaluation procedure is realized by Matlab 7.0 software and the radial basis function(RBF)kernel is selected. Finally, the support vector machine comprehensive evaluation method, the neural network evaluation method and the matter-element evaluation method are applied to evaluate the development level of transportation network of the Pearl River Delta and the Yangtze River Delta economic circles. The results show that the support vector machine comprehensive evaluation method has a significant effect in multi-pattern recognition and small sample data classification, and it is more efficient and more accurate than the evaluation method based on BP neural network when appropriate kernel function and corresponding punishment parameters are selected. Therefore, the proposed evaluation method can efficiently evaluate the development level of the transport network in economic circle.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 张兵(1981—),男,博士,讲师,zbing1981@sohu.com.
基金项目: 国家高技术研究发展计划(863计划)资助项目(2007AA11Z202).
引文格式: 张兵,邓卫.经济圈交通网络SVM评价方法[J].东南大学学报:自然科学版,2012,42(6):1227-1232. [doi:10.3969/j.issn.1001-0505.2012.06.037]
更新日期/Last Update: 2012-11-20