Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

Abstract

To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown, an optimized model based on support vector machine (SVM) is put forward firstly to enhance the quality of product in hot strip rolling. Meanwhile, for enriching data information and ensuring data quality, experimental data were collected from a hot-rolled plant to set up prediction models, as well as the prediction performance of models was evaluated by calculating multiple indicators. Furthermore, the traditional SVM model and the combined prediction models with particle swarm optimization (PSO) algorithm and the principal component analysis combined with cuckoo search (PCA-CS) optimization strategies are presented to make a comparison. Besides, the prediction performance comparisons of the three models are discussed. Finally, the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed. Furthermore, the root mean squared error (RMSE) of PCA-CS-SVM model is 2.04 µm, and 98.15% of prediction data have an absolute error of less than 4.5 µm. Especially, the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

摘要

为了弥补传统控制方法的缺陷,满足日益增长的板凸度精度要求,提出了一种基于支持向量机 (SVM)的优化模型,以提高热轧带钢产品的质量。为了丰富数据信息并保证数据质量,建立预测模型 的实验数据均来自于某热轧厂,并通过计算多项评价指标来评估模型的预测性能。建立主成分分析结 合布谷鸟搜索(PCA-CS)算法优化的预测模型,并与粒子群优化算法(PSO)优化的模型及传统SVM 模 型进行对比,分析并讨论了这三种模型的预测性能。实验结果表明,PCA-CS-SVM 模型具有最高的预 测精度和最快的收敛速度,模型的均方根误差(RMSE)为2.04 μm,且98.15%的预测数据的绝对误差小 于4.5 μm。结果证明,PCA-CS-SVM 模型不仅能够满足板凸度精度要求,而且对热轧带钢的实际生 产具有一定指导意义。