In the hot strip rolling process, accurate prediction of bending force can improve the control accuracy of the strip flatness and further improve the quality of the strip. In this paper, based on the production data of 1300 pieces of strip collected from a hot rolling factory, a series of bending force prediction models based on an extreme learning machine (ELM) are proposed. To acquire the optimal model, the parameter settings of the models were investigated, including hidden layer nodes, activation function, population size, crossover probability, and hidden layer structure. Four models are established, one hidden layer ELM model, an optimized ELM model (GAELM) by genetic algorithm (GA), an optimized ELM model (SGELM) by hybrid simulated annealing (SA) and GA, and two-hidden layer optimized ELM model (SGITELM) optimized by SA and GA. The prediction performance is evaluated from the mean absolute error (MAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The results show that the SGITELM has the highest prediction accuracy in the four models. The RMSE of the proposed SGITELM is 11.2678 kN, and 98.72% of the prediction data have an absolute error of less than 25 kN. This indicates that the proposed SGITELM with strong learning ability and generalization performance can be well applied to hot rolling production.

中文翻译：

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多层极限学习机预测带钢热轧过程中的弯曲力

在带钢热轧过程中，弯曲力的准确预测可以提高带钢平直度的控制精度，并进一步提高带钢的质量。本文基于从热轧厂收集的1300条带钢的生产数据，提出了一系列基于极限学习机（ELM）的弯曲力预测模型。为了获得最佳模型，研究了模型的参数设置，包括隐藏层节点，激活函数，种群大小，穿越概率和隐藏层结构。建立了四个模型，一个是隐层ELM模型，一个是通过遗传算法（GA）优化的ELM模型（GAELM），一个是通过混合模拟退火（SA）和GA进行优化的ELM模型（SGELM），并且是两个隐藏层优化的ELM模型（SGITELM）由SA和GA优化。根据平均绝对误差（MAE），均方根误差（RMSE）和平均绝对百分比误差（MAPE）评估预测性能。结果表明，SGITELM在四个模型中具有最高的预测精度。提出的SGITELM的RMSE为11.2678 kN，并且98.72％的预测数据的绝对误差小于25 kN。这表明所提出的具有较强学习能力和泛化性能的SGITELM可以很好地应用于热轧生产。72％的预测数据的绝对误差小于25 kN。这表明所提出的具有较强学习能力和泛化性能的SGITELM可以很好地应用于热轧生产。72％的预测数据的绝对误差小于25 kN。这表明所提出的具有较强学习能力和泛化性能的SGITELM可以很好地应用于热轧生产。