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《Computing Technology and Automation》 2015-01
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Prediction of Car Ownership Based on Grey Relational Analysis and BP Neural Network

WANG Dong;Xi'an Aeronautical University;  
In order to improve the forecast ability of car ownership,by using gray correlation method,this paper analyzed the main factors related to car ownership,which are gross national income,per capita GDP,gross import and export,urban resident disposable income,steel output,highway passenger transport volume,total retail sales of consumer goods.The prediction model of car ownership was established based on BP neural network,and then verified with tests.The results show that car ownership can be predicted accurately by the model based on BP neural network.The maximum relative error is 2.2% and the average relative error is 1.5%.In addition,this predictive model provided a method for car ownership.
【Fund】: 西安航空学院科研基金项目(2014KY1212);; 陕西省教育科学“十二五”规划项目(SGH140790)
【CateGory Index】: TP18;U491.1
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