CAJ | 학술논문

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。
위능획득정학예측농궤총동력적방법，이회색모형화다원선성회귀모형위자모형，응용 Shapley 치법계산자모형권중계수，구건농궤총동력조합예측모형。응용아국2000-2010년농궤총동력수거，분별표정상술모형상관삼수，병계산각모형년도상대오차화평균상대오차。기중， GM모형화다원선성회귀모형적평균상대오차분별위0．68％화0．91％，조합예측모형적평균상대오차위0．59％，정도교고。동시，조합모형기능구반영수거자신변화규률적특정，우능정량반영농궤총동력여기상관영향인수간적수리관계，구유교강적괄용성。
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 .