CAJ | 학술논문

综合分析了影响汽柴油消费需求的关键因素,并针对其具有自相关性、复杂性、数据量大等特点,采用主成分分析法对样本数据进行降维处理,形成新的样本集。对支持向量机预测模型进行改进,在其基础之上引入时序动态因子,将上年的汽柴油需求历史数据作为时序反馈因子引入模型,从而形成新的动态反馈拟合模型,建立相应的需求预测模型。对1996~2012年的汽柴油需求预测进行实例研究,并将本文中所提方法的预测结果与灰色GM（1,1）模型、BP神经网络模型进行对比分析。结果表明本文中的主成分分析与改进支持向量机预测方法相对于GM（1,1）模型其预测误差均值分别降低了72.7%和74.86%,相对于BP神经网络其预测误差均值分别降低了81.3%和81.66%,从而证明了此方法的有效性和优越性。
종합분석료영향기시유소비수구적관건인소,병침대기구유자상관성、복잡성、수거량대등특점,채용주성분분석법대양본수거진행강유처리,형성신적양본집。대지지향량궤예측모형진행개진,재기기출지상인입시서동태인자,장상년적기시유수구역사수거작위시서반궤인자인입모형,종이형성신적동태반궤의합모형,건립상응적수구예측모형。대1996~2012년적기시유수구예측진행실례연구,병장본문중소제방법적예측결과여회색GM（1,1）모형、BP신경망락모형진행대비분석。결과표명본문중적주성분분석여개진지지향량궤예측방법상대우GM（1,1）모형기예측오차균치분별강저료72.7%화74.86%,상대우BP신경망락기예측오차균치분별강저료81.3%화81.66%,종이증명료차방법적유효성화우월성。
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.