【作者】 王玲； 郭辉； 付冬梅；

【Author】 WANG Ling 1,GUO Hui,2,FU Dong-Mei 1 1 Information Engineering School,University of Science and Technology Beijing 100083,P.R.China2 Capital Engineering and Research Incorporation Limited Beijing 100176,P.R.China

【机构】 北京科技大学信息工程学院； 中冶京诚工程技术有限公司；

【摘要】 提出了一种基于核偏最小二乘(Kernel partial least square,KPLS)的钢材淬透性预测建模方法。采用KPLS方法对样本数据进行特征提取,消除样本数据输入变量之间的相关性,并引入了免疫遗传算法优化KPLS参数的方法。在此基础上,使用KPLS回归方法建立淬透性预测模型。以某钢厂实际生产数据进行了应用实验,并与现有的多元回归方法、人工神经网络和标准LS-SVM建模方法进行了对比,实验结果表明,本文方法显著提高了钢材淬透性的预测命中率,获得精确的建模效果。更多还原

【Abstract】 Based on the Kernel partial least square(KPLS),a modeling method for the prediction steel quenching degree is proposed in this paper.In order to eliminate the correlations among production parameters,the KPLS is used for feature extraction and Immune genetic algorithm is introduced to optimize the model.With the help of KPLS regression method,a model is then built for the prediction of steel quenching degree.An application study is carried out on the real production data acquired from a steel-making plant.Compared with the existing multiple regression analysis,neural network methods and the ordinary LS-SVM modeling methods,the experimental result shows that the prediction-hit-ratio of the presented method is greatly improved and the precise modeling effect is obtained.更多还原