CAJ | 학술논문

对常用作物产量预测模型进行了简要评述,建立了基于最小二乘支持向量机的灌区产量预测模型.对灌区作物产量进行模拟计算,并用检验样本与灰色预测和神经网络模型的预测结果进行了比较.结果表明,最小二乘支持向量机预测的最大误差7.12%,平均误差4.81%.最小二乘支持向量机模型有较高的预测精度和良好的推广能力,可做为灌区粮食产量预测的一种新方法.
대상용작물산량예측모형진행료간요평술,건립료기우최소이승지지향량궤적관구산량예측모형.대관구작물산량진행모의계산,병용검험양본여회색예측화신경망락모형적예측결과진행료비교.결과표명,최소이승지지향량궤예측적최대오차7.12%,평균오차4.81%.최소이승지지향량궤모형유교고적예측정도화량호적추엄능력,가주위관구양식산량예측적일충신방법.
Commonly used grain yield forecasting models were briefly reviewed, and a yield prediction model of irrigation district was established based on least squares support vector machines. The grain yield in irrigation district was analog calculated. And the test samples were used to compare with gray prediction, and neural network model. The maximum predicted error of least squares SVM was 7.12%, with an average error of 4.81%. The results showed that least squares support vector machine model has high prediction accuracy and strong generalization ability. So it could be used as a new method for irrigation district yield prediction.