Neural Control of a Steel Rolling Mill

IEEE Control Systems

Volumn 13, Issue 3, 1993, Pages 69-75

  Sbarbaro Hofer, D ^a   Neumerkel, D ^a   Hunt, K ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; ROLLING MILLS; STEEL;

ARTIFICIAL NEURAL NETWORKS; FEEDBACK CONTROL;

CONTROL SYSTEMS;

EID: 0027607923     PISSN: 1066033X     EISSN: None     Source Type: Journal    
DOI: 10.1109/37.214948     Document Type: Article

References (7)

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2. M. Roscheisen, R. Hofmann, and V. Tresp, “Neural control for rolling mills: Incorporating domain theories to overcome data deficiency,” Advances in Neural Information Processing (vol. 4), Houson and Lippman, Eds. Morgan Kaufman, 1992, p. 659.
3. Ah Chung Tsoi, “Application of neural network methodology to the modelling of the yield strength in a steel plate mill,” Advances in Neural Information Processing (vol. 4) Houson and Lippman, Eds. Morgan Kaufman, 1992. p. 698.
4. M.L. Jordan and D.E. Rumelhart, “Forward models: Supervised learning with a distal teacher,” M.I.T., Int. pap. 40, 1990.
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7. D.C. Psichogios and L.H. Ungar, “Nonlinear internal model control and model predictive control using neural networks,” in Proc. 5th IEEE Int. Symp. Intelligent Control (vol. 2), 1990, p. 1082.