基于减法聚类的带钢厚度数据驱动建模  

Online data-driven modeling for strip thickness based on subtractive clustering

作　　者：梁辉[1,2] 童朝南[1,2] 

机构地区：[1]北京科技大学自动化学院,北京100083 [2]北京科技大学钢铁流程先进控制教育部重点实验室,北京100083

出　　处：《北京科技大学学报》2012年第11期1338-1345,共8页Journal of University of Science and Technology Beijing

基　　金：北京市教委重点学科资助项目(XK100080537)

摘　　要：针对轧钢生产中大批过程数据没有被用于提高厚度质量的现象,提出了一种基于减法聚类的带钢厚度数据驱动在线建模方法.首先通过减法聚类将输入空间划分为一些小的局部空间,在每个局部空间中用最小二乘支持向量机建立子模型,子模型加权输出作为带钢厚度的离线模型;然后当在线数据不断增加时,通过在线减法聚类算法实时调整局部空间,子模型的参数采用最小二乘支持向量机的递推算法进行相应的在线辨识,子模型的预测输出作为模型的最后输出.实验结果表明,该方法具有良好的预测精度和较强的在线学习能力.In hot rolling,actual production data were not used to improve the thickness quality of products.For this phenomenon,an online data-driven modeling algorithm was proposed for strip thickness control based on subtractive clustering.Firstly,the input space is divided into several clusters by subtractive clustering,in each cluster a sub-model is built by a least square support vector machine（LS-SVM）,and an offline model is obtained by weighting the outputs of these sub-models.Then,when the online data constantly increase,the clustering subsets are adjusted on-line by a subtractive clustering algorithm,and the parameters of the sub-models are determined using the recursive algorithm of the least squares support vector machine.The predictive outputs of the sub-models are the final outputs.Experimental results demonstrate that the method has good prediction accuracy and online learning ability.

关 键 词：热轧 厚度控制 数据驱动 聚类算法 最小二乘法 支持向量机 

分 类 号：TP273.1[自动化与计算机技术—检测技术与自动化装置]