摘要
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。
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 resuhs 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.
出处
《安徽农业科学》
CAS
北大核心
2010年第1期98-100,共3页
Journal of Anhui Agricultural Sciences
基金
国家863计划项目(2006AA100213)
国家科技支撑计划项目(2007BAD38B04)
关键词
产量
预测
最小二乘支持向量机
模型
Yield
Forecast
Least squares support vector machine
Model
