Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace

This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten iron quality (MIQ) in blast furnace (BF) ironmaking. First, in order to accurately describe the importance of different process variables on t...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 17; S. 10425 - 10438
Hauptverfasser:	Zhou, Ping, Zhao, Peng, Ou, Zihui, Chai, Tianyou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.06.2025 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Coal Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Consumption Data Mining and Knowledge Discovery Distributed control systems High temperature Image Processing and Computer Vision Iron Iron compounds Ironmaking Neural networks Prediction models Probability and Statistics in Computer Science Process variables Regularization S.I.: Timely Advances of Deep Learning with applications and Data-Driven Modeling Special Issue on Timely Advances of Deep Learning with applications and Data-Driven Modeling Sulfur content Variables Attention mechanism Random vector functional-link networks (RVFLNs) Blast furnace (BF) ironmaking Stacked semi-supervised autoencoder Reliable prediction of molten iron quality (MIQ)
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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