(燕山大学 工业计算机控制工程河北省重点实验室，秦皇岛 066004)
摘 要: 在铝热轧过程中，轧制力预报精度直接影响着成品的产量和质量。为了提高铝热连轧轧制力预报精度，提出一种基于深度学习方法的多层感知器(Multi-layer Perceptron，MLP)轧制力预报模型。模型利用MLP的函数逼近能力来回归轧制力。模型以小批量训练为基础，利用Batch Normalization方法稳定网络前向传播的输出分布，并使用Adam随机优化算法来完善梯度更新，以解决MLP模型难以训练的问题。仿真结果表明：模型使网络预测与实测数据的相对误差降低到3%以内，实现了轧制力的高精度预测。
关键字: 铝热轧；轧制力预测；深度学习；多层神经网络；优化算法
（Key Lab of Industrial Computer Control Engineering Department of Yanshan University, Qinhuangdao 066004, China）
Abstract:In the aluminum hot rolling, the prediction accuracy of the rolling force directly affects the output and quality of the finished product. In view of the inherent defects of traditional rolling force model, a MLP rolling force prediction model based on deep learning method was proposed. The model uses MLP’s function approximation ability to regress the rolling force. Based on the Mini-batch training, the model uses Batch Normalization method to stabilize the output distribution of the network forward propagation, and uses the Adam stochastic optimization algorithm to improve the gradient updating so as to solve the difficult training problem of the MLP model. The simulation results show that the model can reduce the relative error between the network prediction and the measured data to less than 3%. Compared with the traditional mathematical model, this method realizes the high precision prediction of the rolling force, and realizes a high-precision prediction of rolling force.
Key words: aluminum hot rolling; rolling force prediction; deep learning; multilayer neural network; optimization algori
