Abstract
The elongation of steel-strips in annealing furnace is an important factor that affects the position of welding line and safety of air-knife since there is no extra space to install welding line detector in field conditions. Therefore, predicting the elongation of steel-strips in the annealing process is important to fulfill the requirements of eliminating security risks and improving economic performance. In this paper, we propose a deep architectures called I-ELM/MLCSA autoencoders with the concept of stacked generalization philosophy to solve large and complex data mining problems. The comparison results of the case studies indicate that D-ELMs-AE/MLCSA is a promising prediction algorithm and can be employed for steel-strips elongation predictions with excellent performance.
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Recommended by Associate Editor Huaping Liu under the direction of Editor Euntai Kim. This work was supported by the National Natural Science Foundation of China (61102124), Liaoning Key Industry Programme(JH2/101).
Chao Wang received his B.S. and M.S. degrees in Electrical Engineering from Shenyang Jianzhu University, China, in 2008 and 2011, respectively, where he is currently pursuing a Ph.D. degree in Northeastern University, China. His research interests include computational intelligence, intelligent control, and machine learning.
Jian-Hui Wang received her B.S., M.S., and Ph.D. degrees in Electrical Engineering from Northeastern University, China, in 1982, 1986, and 1999, respectively. Her research interests include intelligent control theory and its application.
Shu-Sheng Gu received his B.S. in Automation from Northeastern University, China, in 1969. His research interests include intelligent control theory and its application.
Xiao Wang received the B.S in automation from the college of information science and engineering, Northeastern University of China in 2013, where he is pursuing the Ph.D. degree in control theory and engineering. His research interests include the applications of advanced controls in wind turbine system, and the transient stability of the power system related to large-scale wind power integrations.
Zhang Yuxian received his Ph.D degree in 2007 from Northeastern University, and finished his postdoctoral study in 2009 from Tsinghua University. Now, he is an associate professor in Shenyang University of Technology. His main research interests include intelligent control, optimal control and data mining for hybrid data.
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Wang, C., Wang, JH., Gu, SS. et al. Elongation prediction of steel-strips in annealing furnace with deep learning via improved incremental extreme learning machine. Int. J. Control Autom. Syst. 15, 1466–1477 (2017). https://doi.org/10.1007/s12555-015-0463-7
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DOI: https://doi.org/10.1007/s12555-015-0463-7