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Prediction Model of Steel Mechanical Properties Based on Integrated KPLS

  • Ling Wang
  • Hui Zhu
  • Ruixia Huang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

In this paper, an integrated KPLS (Kernel Partial Least Square) prediction model for steel mechanical property is proposed. To eliminate the heterogeneity among variables in the hot rolling process, the KFA (Kernel Factor Analysis) is used to obtain the latent factor load vectors. Then the variables with large factor load were clustered into subsets, and the KPLS components are extracted respectively for each subset variable and target variable. Finally, the KPLS results of all subsets were integrated as input, and an integral KPLS prediction model is constructed with the target variables. An application study was carried out on the real production data of a steel-making plant. The experimental result shows that the precision of the presented method is greatly improved.

Keywords

Kernel factor analysis Kernel partial least square Steel mechanical property 

Notes

Acknowledgements

This research work was supported by the National Natural Science Foundation of China (Grant No. 61572073), National Key R&D Program of China (NO. 2017YFB0306403) and the Fundamental Research Funds for the China Central Universities of USTB (FRF-BD-17-002A).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Automation & Electrical EngineeringUniversity of Science and TechnologyBeijingChina

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