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

作　　者：刘静[1] 朱达荣[1] 

机构地区：[1]安徽建筑大学机械与电气工程学院,合肥230601

出　　处：《农机化研究》2015年第4期230-236,共7页Journal of Agricultural Mechanization Research

基　　金：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(20120111120021)

摘　　要：为能获得精确预测农机总动力的方法,以灰色模型和多元线性回归模型为子模型,应用Shapley值法计算子模型权重系数,构建农机总动力组合预测模型。应用我国2000-2010年农机总动力数据,分别标定上述模型相关参数,并计算各模型年度相对误差和平均相对误差。其中,GM模型和多元线性回归模型的平均相对误差分别为0.68%和0.91%,组合预测模型的平均相对误差为0.59%,精度较高。同时,组合模型既能够反映数据自身变化规律的特征,又能定量反映农机总动力与其相关影响因数间的数理关系,具有较强的适用性。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 Agricultural Machinery Total Power data in China from 2000 to 2010 , these models were calibrated parameters , then the relative 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 

分 类 号：S210.7[农业科学—农业机械化工程]