Cold rolling mill thickness control using the cascade-correlation neural network

Frayman, Yakov, Wang, Lipo and Wan, Chunru 2002, Cold rolling mill thickness control using the cascade-correlation neural network, Control and cybernetics, vol. 31, no. 2, pp. 327-342.

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Title Cold rolling mill thickness control using the cascade-correlation neural network
Author(s) Frayman, Yakov
Wang, Lipo
Wan, Chunru
Journal name Control and cybernetics
Volume number 31
Issue number 2
Start page 327
End page 342
Publisher Polish Academy of Sciences, Systems Research Institute
Place of publication Warszawa, Poland
Publication date 2002
ISSN 0324-8569
Keyword(s) direct MRAC
cascade-correlation neural network
dynamic neural network construction
cold roIling mill thickness control
Summary The improvements in thickness accuracy of a steel strip produced by a tandem cold-roIling mill are of substantial interest to the steel industry. In this paper, we designed a direct model-reference adaptive control (MRAC)  scheme that exploits the natural level of excitation existing in the closed-loop with a dynamically constructed cascade-correlation neural network (CCNN) as a controller for cold roIling mill thickness control. Simulation results show that the combination of a such a direct MRAC scheme and the dynamically constructed CCNN significantly improves the thickness accuracy in the presence of disturbances and noise in comparison with to the conventional PID controllers.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
HERDC Research category C1 Refereed article in a scholarly journal
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Document type: Journal Article
Collection: School of Information Technology
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