基于主成分分析协同随机森林算法的热连轧带钢宽度预测

Prediction of Rough Rolling Width Based on Principal Component Analysis Collaborated with Random Forest Algorithm

为提高热连轧粗轧带钢生产过程中换钢种、换规格及换辊后的首块带钢宽度设定模型精度,本文提出一种基于主成分分析协同随机森林(PCA-RF)算法的宽度预测模型.采用主成分分析法对数据样本合理分析,通过计算特征值、主成分贡献度及累计贡献度进行特征选择.在PCA筛选的变量数据集上训练最佳随机森林宽度预测模型.同时,使用支持向量机回归(SVR)、K-最近邻(KNN)模型进行对比验证.通过实际应用表明,PCA-RF各道次宽度模型R-squared值控制在99.9%~1,且96%以上样本点预测误差在-5~5 mm,从而证明该模型实现了换钢种、换规格及换辊后的首块钢宽度的高精度预测.

To improve the accuracy of the predicted width of the first piece of steel after changing the steel type,the steel specification and the roll in the process of hot continuous rough rolling strip production,a new width prediction model based on the principal component analysis collaborated with random forest(PCA-RF)algorithm is proposed in this work.The PCA method is used to analyze the reasonability of data samples and the feature selection is carried out by calculating the eigenvalue,and principal component and cumulative contribution degrees.The best RF model is trained on variant dataset selected from the PCA.At the same time,support vector machine regression(SVR)and K-nearest neighbor(KNN)models are used for comparison and verification.The practical applications show that the R-squared value from the each pass width predicted by the PCA-RF model is controlled within the range of 0.999~1,and the prediction error of more than 96%samples is-5~5 mm,which proves that the model can predict the steel width with a high precision.