Classification of silicon content variation trend based on fusion of multilevel features in blast furnace ironmaking

•A data-driven model is proposed based on multilevel features fusion to classify the variation trend of silicon content.•Multilevel features are extracted through variable analysis, statistical information, and stacked denoising autoencoder, respectively.•Silicon content trend labels are quantitativ...

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Vydané v:	Information sciences Ročník 521; s. 32 - 45
Hlavní autori:	Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Chen, Zhipeng, Pan, Dong, Gui, Weihua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.06.2020
Predmet:	Classification Denoising autoencoder (DAE) Multilevel features fusion Recurrent neural network (RNN) Variation trend for silicon content Denoising autoencoder (DAE) Multilevel features fusion Variation trend for silicon content Recurrent neural network (RNN) Classification
ISSN:	0020-0255, 1872-6291
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