关键词: 车辆工程, 汽车保有量, 背景值, Simpson公式的GM(1, 1)模型, 组合预测模型, 预测精度

Abstract: According to the features of non-linearity and randomness of car ownership data, a grey neural network model with Simpson formula was established to predict Chinas car ownership. First, the formula was employed to construct the background value of classic GM (1,1) for improving prediction accuracy; second, such seven factors of car ownership as total gross national income, per capita gross domestic product, total population, investment in fixed assets, total import and export, steel output and total retail sales of consumer goods were viewed by correlation analysis and then the input of neural network was used for building BP neural network model. To determine the weight of the combined model with the prediction error of grey system and BP neural network, the grey neural network model was constructed. With the comparison among the classic grey GM (1,1), GM (1,1) of Simpson formula, BP neural network, grey neural network, Simpson formula grey neural network model, the results show that grey neural network with Simpson formula has the highest prediction accuracy where the relative error is as low as 3% and the mean square deviation is 3.2780%, which is smaller than grey neural network model and single prediction model.

Key words: vehicle engineering, car ownership, background value, GM (1,1) model of Simpson formula, combined forecasting model, prediction accuracy