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Utilize este identificador para citar ou criar um link para este item: http://acervodigital.unesp.br/handle/11449/40220
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dc.contributor.authordos Santos Filho, Antonio Luiz-
dc.contributor.authorRamirez-Fernandez, Francisco Javier-
dc.date.accessioned2014-05-20T15:30:57Z-
dc.date.accessioned2016-10-25T18:06:38Z-
dc.date.available2014-05-20T15:30:57Z-
dc.date.available2016-10-25T18:06:38Z-
dc.date.issued2010-03-01-
dc.identifierhttp://dx.doi.org/10.1016/j.engappai.2009.10.004-
dc.identifier.citationEngineering Applications of Artificial Intelligence. Oxford: Pergamon-Elsevier B.V. Ltd, v. 23, n. 2, p. 169-176, 2010.-
dc.identifier.issn0952-1976-
dc.identifier.urihttp://hdl.handle.net/11449/40220-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/40220-
dc.description.abstractThis paper traces the development of a software tool, based oil a combination of artificial neural networks (ANN) and a few process equations. aiming to serve as a backup operation instrument in the reference generation for real-time controllers of a steel tandem cold mill By emulating the mathematical model responsible for generating presets under normal operational conditions, the system works as ail option to maintain plant operation in the event of a failure in the processing unit that executes the mathematical model. The system, built from the production data collected over six years of plant operation, steered to the replacement of the former backup operation mode (based oil a lookup table). which degraded both product quality and plant productivity. The study showed that ANN are appropriated tools for the intended purpose and that by this instrument it is possible to achieve nearly the totality of the presets needed by this land of process. The text characterizes the problem, relates the investigated options to solve it. justifies the choice of the ANN approach, describes the methodology and system implementation and, finally, shows and discusses the attained results. (C) 2009 Elsevier Ltd. All rights reserveden
dc.format.extent169-176-
dc.language.isoeng-
dc.publisherPergamon-Elsevier B.V. Ltd-
dc.sourceWeb of Science-
dc.subjectIntelligent automationen
dc.subjectArtificial neural networksen
dc.subjectCold rollingen
dc.subjectMill setupen
dc.titleA neural network-based preset generation tool for a steel tandem cold millen
dc.typeoutro-
dc.contributor.institutionSão Paulo Fed Inst Educ Sci & Technol-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationSão Paulo Fed Inst Educ Sci & Technol, Ind Syst Dept, BR-11533160 São Paulo, Brazil-
dc.description.affiliationSão Paulo State Univ, Polytech Sch, Dept Elect Syst Engn, BR-05508900 São Paulo, Brazil-
dc.description.affiliationUnespSão Paulo State Univ, Polytech Sch, Dept Elect Syst Engn, BR-05508900 São Paulo, Brazil-
dc.identifier.doi10.1016/j.engappai.2009.10.004-
dc.identifier.wosWOS:000275785300004-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofEngineering Applications of Artificial Intelligence-
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