基于灰色关联分析的支持向量机的铁路货运量预测研究
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引用本文:张 蕾,孙德山,张文政,王 玥.基于灰色关联分析的支持向量机的铁路货运量预测研究[J].经济数学,2018,(2):58-61
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张 蕾,孙德山,张文政,王 玥 (辽宁师范大学 数学学院,辽宁 大连 116029) 
中文摘要:采用基于灰色关联分析的支持向量机对铁路货运量进行预测.首先利用灰色关联分析法对影响铁路货运量的因素进行分析处理,然后利用基于高斯核函数的支持向量回归机建立了铁路货运量预测模型.通过分析预测结果可以发现,经过灰色关联分析后的支持向量机模型对复杂的铁路货运量数据有较好地处理能力,且预测相对误差较小.特别地,由于支持向量机的适应性,该模型具有较高的泛化能力,对影响因素较为复杂,样本数量小的预测问题可以提供一定参考.
中文关键词:铁路货运量预测  灰色关联分析  支持向量机
 
Research on the Rail freight Volumes Forecast by Support Vector Machine and Gray Relational Analysis
Abstract:Using the method of support vector machine which is based on grey correlation analysis to predict railway freight volume. Firstly, using the gray correlation analysis method to analyze the influencing factors of railway freight volume, Secondly using the support vector regression which is based on the Gauss kernel function to establish the prediction model of the volume of railway freight. By analyzing the prediction results, we can find that the support vector machine model which is analyzed by the gray correlation analysis method can process the complex date of the volume of railway freight well, and the relative error of the prediction is relatively smal. Especially, due to the adaptability of support vector machine, the model has a high ability of generalization, and it can provide a reference for the prediction problems with complex factors and small sample size.
keywords:applied mathematics  forecast of railway freight volume  grey relational analysis  support vector machine
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