Predicting the Fineness of Raw Mill Finished Products on the Basis of KPCA-SVM

摘要：

Combining kernel principal component analysis(KPCA)and support vector machines(SVM)in this study,we set up a KPCA-SVM model to predict the fineness of raw mill finished products.We conducted nonlinear feature extraction from the technological parameter samples of the raw mill by means of KPCA and obtained the feature principal components that are easier for regression operations.Thus,the number of input space dimensions that can lower the SVM was met.Then we conducted training by using the least squares support vector machines(LS-SVM).Finally,our computation results proved that the model proposed in this study can effectively predict the fineness of raw mill finished products.

关键词： KPCA SVM fineness of raw mix prediction

作者: SHU Yunxing YUN Shiwei GE Bo

作者单位: Luoyang Institute of Science and Technology ,Luoyan,China;School of Mechatronic Engineering Wuhan Un Luoyang Institute of Science and Technology ,Luoyan,China

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）