NDLI: Modeling for prediction steel mechanical property based on KFA-KPLS

Content Provider	IEEE Xplore Digital Library
Author	Wang Ling Fu Dong Mei Li Qing
Copyright Year	2012
Description	Author affiliation: School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (Wang Ling; Fu Dong Mei; Li Qing)
Abstract	Based on Kernel Factor Analysis (KFA) and Kernel partial least square (KPLS), a modeling method for the prediction of steel mechanical property is proposed in this paper. In order to eliminate the heterogeneity among variables in the hot rolling process, the KFA is used for latent factor load vectors, then the variables with bigger factor load are clustered into sub-clusters, which are reorganized by KPLS with the objective variable. Finally, the results of all KPLS were used as the input of the KPLS model to predict the steel mechanical property. An application study is carried out on the real production data acquired from a steel-making plant. The experimental result shows that the precision of the presented method is greatly improved.
Starting Page	7005
Ending Page	7008
File Size	248744
Page Count	4
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Kernel Factor Analysis Predictive models Educational institutions Mechanical factors Electronic mail Steel Kernel Kernel Partial Least Square Load modeling Steel Mechanical Property
Content Type	Text
Resource Type	Article

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