使用数学模型和BP神经网络相结合的方法对轧制力进行预测。与大多数神经网络仅选取轧制变量作为输入量不同, 该BP神经网络增加了喷油量和轧制力模型计算值作为输入变量以考虑摩擦对轧制力的影响, 避免过大的轧制力预测偏差, 从而形成了11×7×1的网络结构, 并和轧制力模型组合构成CSP生产线轧制力预测模型。结果表明: 该神经网络模型预测值与实测平均值的平均相对误差仅为1.08%, 轧制力模型的平均相对误差为6.32%, 该神经网络对轧制力实测平均值的跟踪能力更好, 有较高的工程应用价值。

The method combining mathematical model and BP neural network was used to predict rolling force. Unlike most neural networks which only selected rolling variables as input variables, the BP neural network added lubricating oil injection quantity and calculated data of rolling force model into the input variables to reflect the impact of friction on rolling force and avoid large rolling force prediction deviation, so the 11×7×1 network structure was established, then the CSP line rolling force prediction model was formed in combination with rolling force model. The results show that the average relative error between the predicted data of the neural network model and the average measured data was only 1.08％, while the average relative error of rolling force model was 6.32%. So it can be concluded that the neural network model had a good ability in tracing average measured rolling force and high value in engineering application.