基于增量学习树模型的带钢精轧宽度预测

Prediction on strip finish rolling width based on incremental learning tree model

针对热轧带钢在轧制过程中出现的由于换辊或其他原因导致的精轧宽展预测模型不精准、热轧领域采集数据维度高和数据量大时训练时间长等问题,提出了一种面向工况漂移的基于增量学习树模型的带钢精轧宽度预测方法。首先,根据传统精轧机理模型得出变工况产生的原因和主要特征参数;然后,将在机理与数据层次上总结得到的有效数据特征作为预测模型的输入,采用增量学习方法建立宽展预测模型;最后,通过实验设计,在实际数据上将增量学习建立的树模型与其他机器学习模型在相同指标上进行对比。结果表明,提出的基于增量学习的树模型在宽展预测中拥有较高的预测精度和较低的运行时间。

Aiming at the problems of inaccurate prediction model of finish rolling width spreading due to roller change or other reasons during the rolling process of hot-rolled strip steel,and long training time when the dimension of data collected was high and the amount of data was large in the field of hot rolling,a width prediction method based on incremental learning tree model for operating condition drift was proposed.First,the causes and main characteristic parameters of the variable working conditions were obtained based on the tradition-al finish rolling mechanism model.Then,the effective data features summarized at the mechanism and data levels were combined as the input of the prediction model,and the width spreading prediction model was established by the incremental learning method.Finally,the tree model established by incremental learning was compared with other machine learning models with the same indicators on actual data by experimental design.The results show that the proposed tree model based on incremental learning has higher prediction accuracy and low running time in wide-spread prediction.