Data feature analysis for blast furnace temperature prediction from machine learning perspective

Author:

Duan Junyi1ORCID

Affiliation:

1. Beijing University of Technology, China

Publisher

ACM

Reference12 articles.

1. Design and Research of Machine Learning Subsystem of Expert system for Determining Blast furnace thermal State;Min Zhu;Iron and steel Research Institute S1.

2. Guo Dan Li Junfang. "Multi-step predictive control for nonlinear systems based on neural network. " PCC 2009 - The 20th China Process Control Conference.

3. Chen Lingkun. "Application of Pattern Recognition Technology in Process Control of Blast Furnace Ironmaking" Wuhan Iron and Steel Technology June 2010: 50-53.

4. Heli Koskim¨aki, Ilmari Juutilainen, Perttu Laurinen, Juha R¨oning. Two-level Clustering Approach to Training Data Instance Selection:a Case Study For the Steel Industry[J]. 2008 International Joint Conference on Neural Networks. 2008: 3044-3049

5. Sravan Tatipala1,Johan Wall1, Christian M Johansson,Mats Sigvant.Data-driven modelling in the era of Industry 4.0: A case studyof friction modelling in sheet metal forming simulations [J]. IOP Conf. Series: Journal of Physics, 2018.

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