Railway Passenger Volume Forecasting Based on Support Vector Machine and Genetic Algorithm

被引：8
|
作者：
Chen, Xiaogang [1 ]
机构：
[1] Huaiyin Inst Technol, Digital Mfg Technol Lab, Huaian, Peoples R China
来源：
2009 ETP INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATION (FCC 2009) | 2009年
关键词：
railway passenger volume; genetic algorithm; support vector machine; forecasting method;
D O I：
10.1109/FCC.2009.81
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new prediction approach for the railway passenger volume is put forward by means of support vector machine optimized by genetic algorithm (GA-SVM). In GA-SVM model, GA is used to determine training parameters of support vector machine. GA has strong global search capability, which can get optimal solution in short time. Railway passenger volume of China from 1985-2002 is used to illustrate the performance of the proposed GA-SVM model. The experimental results indicate that the GA-SVM method can achieve greater forecasting accuracy than artificial neural network in railway passenger volume forecasting.
引用
收藏
页码：282 / 284
页数：3
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