首页> 外文会议>Advances in Neural Networks - ISNN 2007 pt.1; Lecture Notes in Computer Science; 4491 >Application of Neural Network on Rolling Force Self-learning for Tandem Cold Rolling Mills
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Application of Neural Network on Rolling Force Self-learning for Tandem Cold Rolling Mills

机译:神经网络在冷连轧机轧制力自学习中的应用

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摘要

All the factors that influence the rolling force are analyzed, and the neural network model which uses the back propagation (BP) learning algorithm for the calculation of rolling force is created. The initial network's weights corresponding to the input material grades are taught by the traditional theoretical model, and saved in the database. In order to increase the prediction accuracy of rolling force, we use the measured rolling force data to teach the neural network after several coils of the same input material are rolled down.
机译:分析了影响轧制力的所有因素,并建立了使用反向传播学习算法进行轧制力计算的神经网络模型。对应于输入物料等级的初始网络权重由传统的理论模型进行教授,并保存在数据库中。为了提高轧制力的预测精度,我们使用测得的轧制力数据在相同输入材料的多个线圈被轧制后教导神经网络。

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