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Mathematical Problems in Engineering
Volume 2015, Article ID 326160, 11 pages
http://dx.doi.org/10.1155/2015/326160
Research Article

Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback

1State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
2Control System Center, Manchester University, Manchester M60 1QD, UK

Received 21 August 2014; Revised 17 November 2014; Accepted 18 November 2014

Academic Editor: Jiuwen Cao

Copyright © 2015 Ping Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Chao Sun, and David Stirling, “An event group based classification framework for multi-variate sequential data,” Australasian Journal of Information Systems, vol. 21, 2017. View at Publisher · View at Google Scholar
  • Ping Zhou, Li Zhang, Wen-Peng Li, Peng Dai, and Tian-You Chai, “Autoencoder and PCA Based RVFLNs Modeling for Multivariate Molten Iron Quality in Blast Furnace Ironmaking,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 44, no. 10, pp. 1799–1811, 2018. View at Publisher · View at Google Scholar
  • Gaopeng Wang, “Silicon Prediction Model of Blast Furnace Based on ARX and PCR,” Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 2018-, pp. 1214–1220, 2019. View at Publisher · View at Google Scholar