基于Shapley值的农机装备水平组合预测  

Combination prediction of agricultural equipment level based on Shapley value

作　　者：张淑娟[1] 冯屾[1] 介邓飞[1] 王凤花[1] 

机构地区：[1]山西农业大学工程技术学院,太谷030801

出　　处：《农业工程学报》2008年第6期160-164,共5页Transactions of the Chinese Society of Agricultural Engineering

基　　金：山西省高校科技研究开发项目(20051220)

摘　　要：对农机装备水平的定量预测可以为农业机械化发展目标的制定提供依据。该文选用ARIMA时间序列和BP神经网络模型,再基于Shapley值法分配权重,构建了新的组合预测模型,并以1979～2005年山西省农机总动力、大中型拖拉机及配套农机具、小型拖拉机及配套农机具的统计数据为依据进行了预测。预测结果表明,该组合预测模型的预测精度高于选定的各预测模型,对农机装备水平的预测是可行、有效的。以此模型预测山西省2010年农机总动力、大中型拖拉机、小型拖拉机、大中型拖拉机配套农机具、小型拖拉机配套农机具、大中拖拉机配套机具比、小型拖拉机配套农机具比将达到2619万kW、43479台、297546台、84638套、327743套、1.95、1.10。The quantitative prediction of agricultural equipment level can provide the basis for making plan of agricultural mechanization development. ARIMA time series and BP neural network model were chosen to construct a new combination prediction model based on Shapely value method to determine each prediction model weight. By the model, the agricultural equipment level in Shanxi province were predicted according to the total power of agricultural machinery, the large medium tractors, the small tractors, the matching implements of large medium tractors, and the matching implements of small tractors from 1979 to 2005. The results show that the prediction precision of combination prediction model is higher than any selected prediction model, and is feasible and effective for the prediction of agricultural equipment level. The data of the total power of agricultural machinery, the large medium tractors, the small tractors, the matching implements of large medium tractors, and the matching implements of small tractors, the rate of implements of large medium tractors, the rate of small tractors in Shanxi province were predicted ,and will attain to 2619 million kW, 43497 sets, 297546 sets, 84683 sets, 327743 sets, 1.96, 1.15 in 2010 by combination prediction model.

关 键 词：农机装备水平 ARIMA BP神经网络 SHAPLEY值 组合预测 

分 类 号：S23-01[农业科学—农业机械化工程]