ISIJ International
Online ISSN : 1347-5460
Print ISSN : 0915-1559
ISSN-L : 0915-1559
Regular Article
Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process
Ling JianYunquan SongShuqian ShenYan WangHaiqing Yin
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2015 Volume 55 Issue 4 Pages 845-850


Blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction. For this reason, based on recursive updating algorithm, an adaptive least squares support vector machine (LS-SVM) predictor is presented for prediction task of silicon content in blast furnace (BF) hot metal. The predicator employs recursive updating algorithm to get the precise solution of the latest LS-SVM model and avoid the long process of running through the whole model. Theoretically, the computational complexity is reduced significantly from O(n3m + m4) to O(n3 + m3). Experiments on two different BF data sets demonstrate that the proposed adaptive LS-SVM predicator is suitable for the task of predicting BF ironmaking process for its high hitting percentage and time saving.

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© 2015 by The Iron and Steel Institute of Japan
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