基于差分进化支持向量机的轧制力预测  

Rolling force prediction based on differential evolution support vector machine

作　　者：崔桂梅[1] 刘伟[1] 张帅 王磊 CUI Guimei;LIU Wei;ZHANG Shuai;WANG Lei(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;Inner Mongolia Metal Manufacturing Co.,Ltd.,Baotou 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010 [2]内蒙古金属制造有限责任公司,内蒙古包头014010

出　　处：《中国测试》2021年第8期83-88,共6页China Measurement & Test

基　　金：国家自然科学基金(61763039)。

摘　　要：板带材热连轧轧制过程中,轧制力的精确控制对改善带钢板形性能有至关重要的作用。针对B钢厂2250热连轧轧线轧制力模型计算值与实际值误差较大(±10%)的问题,建立基于线性(Linear)核函数、多项式(Poly)核函数、高斯(RBF)核函数3种支持向量机回归预测模型,分析3种核函数支持向量机预测模型,选用拟合效果最好的RBF核函数支持向量机(RBF_SVM)为基础模型。通过差分进化算法对模型的惩罚系数和核函数参数进行最优参数搜索,提高预测模型准确度。实验结果表明,模型轧制力预测值与轧制力实际值误差在±5%的准确率为99.16%,可解决实际轧制过程问题,具有广阔的工程应用前景。In the process of hot continuous rolling of strip steel,the precise control of rolling force plays a vital role in improving strip shape performance.In view of the large error(±10%)between the calculated value and the actual value of the rolling force model of the 2250 hot rolling mill in steel B,we proposed a support vector machine regression prediction model based on the Linear kernel function(Linear),the Polynomial function(Poly)and the Gauss kernel function(RBF).Analyze three kernel function support vector machine prediction models,and select the RBF kernel function support vector machine(RBF_SVM)with the best fitting effect as the basic model.This paper uses differential evolution algorithm to search for the optimal parameters of the model's penalty coefficient C and the kernel function parameter gamma to improve the accuracy of the prediction model.The experimental results show that our model has an accuracy of 99.16%when the error between the predicted value of the rolling force and the actual value of the rolling force is within±5%.It can solve the actual rolling process problems and has broad engineering application prospects.

关 键 词：轧制力预测 核函数 支持向量机 差分进化 

分 类 号：TB9[一般工业技术—计量学] TP391[机械工程—测试计量技术及仪器]