A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems

Width deviation is an important metric for evaluating the quality of a hot-rolled strip in steel production systems. This paper considers a width deviation prediction problem and proposes a Machine-learning and Genetic-algorithm-based Hybrid method named MGH to obtain a prediction model. Existing wo...

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Veröffentlicht in:	Information sciences Jg. 589; S. 360 - 375
Hauptverfasser:	Ji, Yingjun, Liu, Shixin, Zhou, Mengchu, Zhao, Ziyan, Guo, Xiwang, Qi, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.04.2022
Schlagworte:	Feature construction Generalized linear regression Genetic algorithm Hierarchical clustering Hot-rolled strip Width deviation prediction Width deviation prediction Hot-rolled strip Feature construction Generalized linear regression Hierarchical clustering Genetic algorithm
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
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