工业工程2015，Vol.18Issue(2)：20-27,50,9.DOI:10.3969/j.issn.1007-7375.2015.02.004
基于主成分分析与支持向量机的汽柴油需求预测
Application of Support Vector Machines Based on Principal Component Analysis in Gasoline and Diesel Demand Prediction
摘要
Abstract
Firstly, a comprehensive analysis of the key factors affecting consumer demand for gasoline and diesel is made for self-relevance, complexity and data volume, etc.A principal component analysis is made to reduce the dimension of the sample data, and a new set of samples is formed.Then, by improving the support vector machine model and introducing a dynamic factor in the timing of its foundation, and the demand for gasoline and diesel last year historical data into the model as the timing of the feedback factor, thus forming a new dynamic feedback fitting model, an appropriate demand forecasting model is estab-lished.Finally, a case study is made on forecasting demand for gasoline and diesel in the 1996~2012, and the proposed method of predicting and gray GM (1,1) model, and BP neural network model are ana-lyzed.The results show that the improved prediction method relative to the GM support vector machine principal component analysis (1,1) model of the prediction errors are respectively 72.7%, 74.86%low-er, and that comparing with the BP neural network, the prediction errors are reduced on average by 81. 3%,81.66%.Results show that the principal component analysis using improved support vector machine method is superior to existing methods, which proves the effectiveness and superiority of this method.关键词
汽柴油需求/预测/主成分分析/支持向量机Key words
gasoline and diesel demand/prediction/principal component analysis/support vector machines分类
农业科技引用本文复制引用
殷旅江,杨立君,何波..基于主成分分析与支持向量机的汽柴油需求预测[J].工业工程,2015,18(2):20-27,50,9.基金项目
国家自然科学基金资助项目(51375004) （51375004）
教育部人文社会科学研究规划基金资助项目(14YJA630079) （14YJA630079）
湖北汽车工业学院博士科研基金资助项目(BK201408) （BK201408）
