A hybrid neural-network/mathematical prediction model for tandem cold mill

Computers and Industrial Engineering

Volumn 33, Issue 3-4, 1997, Pages 453-456

  Cho, Sungzoon ^a   Jang, Min ^a   Yoon, Sungcheol ^a   Cho, Yongjoong ^a   Cho, Hyungsuk ^b  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b POSCO   (South Korea)

Author keywords

Corrective Neural Network Model; Mathematical Model; Roll Force

Indexed keywords

BACKPROPAGATION; COLD ROLLING MILLS; DATA ACQUISITION; DATA REDUCTION; ERROR CORRECTION; MATHEMATICAL MODELS; STRIP MILLS;

CORRECTIVE NEURAL NETWORK MODEL; ROLL FORCE PREDICTION; TANDEM COLD MILL;

NEURAL NETWORKS;

EID: 0031366062     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0360-8352(97)00166-6     Document Type: Article

References (6)

1. D. Rumelhart G. Hinton and R. Williams. Learning internal representation by error propagation. In D. Rumelhart, J. McClelland, and the PDP Research Group, editors, Parallel Distributed Processing, Vol 1 : Foundations, pages 318-362. MIT Press, Cambridge, MA, 1986.
2. W. Lee. Improvement of set-up model for tandem cold rolling mill. Technical report, RIST, 1994.
3. Hitachi Co Ltd. Guide Book for Mathematical Model of 1st Cold Mill. Japan, 1980.
4. N. Pican and F. Alexandre. Artificial neural networks for the presetting of a steel temper mill. IEEE Expert, pages 22-27, 1996.
5. N. Portmann. Application of neural networks in rolling mill automation. Iron and Steel Engineer, pages 33-36, 1995.
6. M. Yamashita, I. Yarita, H. Abe, T. Mikuriya, and F. Yanagishima. Technologies of flying gauge change in fully continuous cold rolling mill for thin gauge steel strips. IRSID Rolling Conference, 2(1), 1987.