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Elongation Prediction of Steel-Strips in Annealing Furnace with Deep Learning via Improved Incremental Extreme Learning Machine

Chao Wang*, Jian-HuiWang, Shu-Sheng Gu, XiaoWang, and Yu-Xian Zhang
International Journal of Control, Automation, and Systems, vol. 15, no. 3, pp.1466-1477, 2017

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."

Keyword : "Baldwinian learning, Clone selection algorithm, deep learning, elongation prediction, incremental extreme learning machine, Lamarckian learning."

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