热镀锌钢卷力学性能GBDT预报模型

针对热镀锌钢卷力学性能预报建模条件属性选取难、预报精度不足的问题，研究了热镀锌钢卷力学性能梯度提升树(gradient boosting decision tree，GBDT)预报模型。利用互信息差算法综合评估工艺参数、化学成分和钢卷尺寸参数等条件属性的相对重要性以及属性之间冗余性，进行模型条件属性筛选；采用同分布原理进行样本划分，结合网格搜索法和交叉验证法优化模型参数，建立力学性能GBDT预报模型。将GBDT模型预报结果与随机森林(random forest,RF)、AdaBoost算法和BP神经网络的预报结果进行比较，比较表明GBDT模型优于其他模型，90%的数据样本预测的绝对误差小于14.24 MPa，94.6%的数据样本相对误差在6%范围内，具有更高的预测精度。

Research on GBDT prediction model of mechanical properties of hot dip galvanized steel coils

In order to investigate the problems of insufficient mechanical property prediction model accuracy and difficulty in selecting conditional attributes for hot-dip galvanized steel coil, the gradient boosting decision tree (GBDT) prediction model is researched. For screening the input conditional attributes for the prediction model, mutual information difference index is used to comprehensively evaluate the relative importance and redundancy of production process parameters, chemical components and geometrical size of steel coil. The uniform distribution principle is used to segment the data sample set, and the GBDT prediction model is optimized by using the grid search method and cross-validation method. The results of GBDT mechanical property prediction model of hot dip galvanized steel coils are compared with those of random forest (RF) model, AdaBoost model and BP neural network. The comparison results show that the GBDT model is superior to the other three models with higher prediction accuracy, the absolute predicted error of GBDT model for 90th percentile of the prediction samples set is less than 14.24 MPa, and the absolute value of relative error of GBDT model for 94.6th percentile is 6%.