The multiple correlation coefficients of fitting, cross validation and external validation were 0.995,0.859 and 0.945, respectively.
所得优化模型的拟合、交互验证及外部验证的复相关系数分别为0.995、0.859和0.945。
In this paper, soft sensor modeling method based on Least Square SVM (LS SVM) is proposed, and cross validation method is used to select hyper-parameter of LS SVM model.
本文研究了基于最小二乘支持向量机的软测量建模方法,并用交叉验证的方法进行支持向量机参数选择。
Result:The coefficients of correlation of inner cross validation and external validation are both above 0.90, and the RMSECV and RMSEP are both below 0.05.
结果:所建的5个模型对验证集样品水分含量的预测值与实测值的相关系数均在0.90以上,预测误差均方根(RMSEP)均在0.05以下。
The optimal parameters of mathematics model were studied using leave-one-out cross validation method.
利用内部交叉验证和自动优化功能对预测模型进行了优化,确定了最优建模参数。