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Predicting the rolling force in hot steel rolling mill using an ensemble model

conference contribution
posted on 2002-01-01, 00:00 authored by Yakov Frayman, Bernard RolfeBernard Rolfe, Peter HodgsonPeter Hodgson, G Webb
Accurate prediction of the roll separating force is critical to assuring the quality of the final product in steel manufacturing. This paper presents an ensemble model that addresses these concerns. A stacked generalisation approach to ensemble modeling is used with two sets of the ensemble model members, the first set being learnt from the current input-output data of the hot rolling finishing mill, while another uses the available information on the previous coil in addition to the current information. Both sets of ensemble members include linear regression, multilayer perceptron, and k-nearest neighbor algorithms. A competitive selection model (multilayer perceptron) is then used to select the output from one of the ensemble members to be the final output of the ensemble model. The ensemble model created by such a stacked generalization is able to achieve extremely high accuracy in predicting the roll separation force with the average relative accuracy being within 1% of the actual measured roll force.

History

Title of proceedings

Artificial intelligence and applications: proceedings of the second IASTED International Conference

Event

IASTED International Conference (2nd : 2002 : Malaga, Spain)

Pagination

143 - 148

Publisher

ACTA Press

Location

Malaga, Spain

Place of publication

New York, N.Y.

Start date

2002-09-09

End date

2002-09-12

ISBN-13

9780889863521

ISBN-10

0889863520

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2005, Monash University

Editor/Contributor(s)

M Hamza

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