热轧非稳态过程轧制力自学习模型优化

为提高热连轧非稳态过程轧制力的预测精度，提出了一种轧制力自学习模型优化方法. 将模型自学习系数分解为层别学习系数和轧制状态学习系数，表征机架间轧制力预报偏差的遗传特性及实际轧辊状态对模型预报的影响.在系数更新过程中，根据层别距离分别对学习系数进行更新，减小了轧制规格切换时轧制力的预报误差.所提方法已成功应用于某热连轧过程，与原模型相比，优化后的自学习方法的预测偏差从2.8%降低到1.4%，均方差从3.3%降低到1.7%，有效提高了非稳态过程轧制力的预测精度和鲁棒性.

Optimization of Rolling Force Self-learning Model in Unsteady Process of Hot Rolling

In order to improve the predicted precision of rolling force in unsteady process， an optimization method for rolling force self-learning model was proposed. The self-learning coefficient in the model was decomposed into the layer learning coefficient and rolling state learning coefficient， which characterized the genetic characteristics of rolling force prediction deviation between racks， and the effect of actual roll state on the model prediction. In the process of coefficient updating， the learning coefficients were updated according to the layer distance so that the prediction error of rolling force could be reduced， especially when the rolling specifications were switched. The proposed self-learning method has been successfully applied into a hot rolling process. Compared with the original model， the predicted deviation of the optimized self-learning method is reduced from 2.8% to 1.4%， and the mean square error is reduced from 3.3% to 1.7%， which effectively improve the accuracy and robustness of rolling force in unsteady process.