农机化研究Issue(4)：230-236,7.
考虑自变及因变影响的农机总动力组合预测模型
Combined Prediction Model of Agricultural Machinery Total Power Based on Independent and Dependent Variables
摘要
Abstract
In order to obtain accurate method of predicting Agricultural Machinery Total Power , Gray Model and Multiple Linear Regression Model were set to the sub-model , and Shapley Value was applied to calculate the weighting factors of sub-model , then the Combined Prediction Model of Agricultural Machinery Total Power was built .Application of Agri-cultural Machinery Total Power data in China from 2000 to 2010 , these models were calibrated parameters , then the rela-tive errors for each year and the average relative errors were calculated , where the average relative errors of gray model and Multiple Linear Regression Model are 0 .68% and 0 .91%, while the average relative error of Combined Prediction Model is 0 .59%, has high precision .And the Combined Prediction Model not only reflects the variation characteristics of the data itself , but also quantitatively reflects the mathematical relationship of Agricultural Machinery Total Power and its factors, has strong applicability .关键词
农机总动力/灰色模型/多元线性回归模型/Shapley值/组合预测Key words
agricultural machinery total power/gray model/multiple linear regression model/Shapley value/combined prediction分类
农业科技引用本文复制引用
刘静,朱达荣..考虑自变及因变影响的农机总动力组合预测模型[J].农机化研究,2015,(4):230-236,7.基金项目
国家自然科学基金项目（51308177，51178158）；高等学校博士学科点专项科研基金项目 （）
