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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 WebbAccurate 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.
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Title of proceedings
Artificial intelligence and applications: proceedings of the second IASTED International ConferenceEvent
IASTED International Conference (2nd : 2002 : Malaga, Spain)Pagination
143 - 148Publisher
ACTA PressLocation
Malaga, SpainPlace of publication
New York, N.Y.Start date
2002-09-09End date
2002-09-12ISBN-13
9780889863521ISBN-10
0889863520Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2005, Monash UniversityEditor/Contributor(s)
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