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

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.