农机化研究2017，Vol.39Issue(8)：38-42,5.
灰色补偿BP神经网络预测农机总动力-以吉林省为例
Gray Compensation BP Neural Network Prediction of the Total Power of Agricultural Machinery-Taking Jilin Province as an Example
摘要
Abstract
The prediction of the total power of agricultural machinery is of great significance and research value to the"supply side"of agricultural machinery .Scientific and reasonable forecast results have important guiding significance for the planning and development of the functional departments .The dynamic data of agricultural machinery has time se-ries properties , and the grey GM ( 1 ,1 ) model is used to analyze the dynamic data effectively .In order to improve the accuracy of prediction , BP neural network is used to deal with the grey residual data , and the grey prediction results are compensated , and the corresponding prediction model is established .Through experiments , it shows that the model is scientific and effective for the prediction of the total power of agricultural machinery in Jilin province .And Jilin province in the next five years , the total power of agricultural machinery to predict , to provide a scientific basis for the relevant policy formulation .关键词
农机总动力/预测/BP神经网络/灰色理论Key words
total power of agricultural machinery/prediction/BP neural network/grey theory分类
农业科技引用本文复制引用
艾洪福..灰色补偿BP神经网络预测农机总动力-以吉林省为例[J].农机化研究,2017,39(8):38-42,5.基金项目
吉林省教育厅“十二五”规划项目（吉教科合字［2015］第183号）；吉林省高等教育学会科研项目（JGJX2015D34）；吉林省教育厅科学研究项目 （）
