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. Attached Files Name Description MIMEType Size Downloads 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30001753 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 23 May 2022, 16:18:18 EST Fri, 17 Oct 2014, 08:09:17 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 610 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 07 Jul 2008, 08:05:46 EST