基于多模态和加权支持向量机的热轧轧制力智能预报

为了提高热轧生产过程精轧机组的轧制力预设定精度，需要对轧制力进行高精度的预报.本文通过机理公式计算出轧制力的近似值，然后采集大量的实际生产数据修正轧制力预报值.首先利用聚类方法区分不同的生产状态，其次在相同生产状态下采用加权最小二乘支持向量机计算轧制力的修正系数，最后采用乘法方式修正轧制力，达到高精度的轧制力预测.结果表明，轧制力预报的平均相对误差为3.2%，满足现场的生产要求.

Intelligent prediction of rolling force in hot rolling based on a multi-model and weighted support vector machine

In order to improve the set accuracy of rolling force for a finishing mill in the hot rolling process, high precision predic-tion of rolling force is very important. In this paper, an approximate value of rolling force is calculated through theoretical formulae. And then, a correction coefficient of rolling force is computed using big field data. Firstly, different product states are classfied by the clustering method. Secondly, the correction coefficient is computed based on a weighted least square support vector machine. Through a combination of these two results, the rolling force value with high precision is predicted. The average relative prediction error of roll-ing force is 3. 2%, which can meet the requirements of field production.