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dc.contributor.authorSedano, Javier
dc.contributor.authorCorchado, Emilio 
dc.contributor.authorCuriel Herrera, Leticia Elena 
dc.contributor.authorVillar, José Ramón
dc.contributor.authorBravo Díez, Pedro Miguel 
dc.date.accessioned2024-02-02T12:50:28Z
dc.date.available2024-02-02T12:50:28Z
dc.date.issued2009
dc.identifier.issn0020-7160
dc.identifier.urihttp://hdl.handle.net/10259/8562
dc.description.abstractReal-world processes may be improved through a combination of artificial intelligence and identification techniques. This work presents a multidisciplinary study that identifies and applies unsupervised connectionist models in conjunction with modelling systems. This particular industrial problem is defined by a data set relayed through sensors situated on a robotic drill used in the construction of industrial storage centres. The first step entails determination of the most relevant structures in the data set with the application of the connectionist architectures. The second step combines the results of the first one to identify a model for the optimal working conditions of the drilling robot that is based on low-order models such as black box that approximate the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box–Jenkins algorithm, which calculates the function of a linear system from its input and output samples.en
dc.description.sponsorshipThis research has been partially supported by project BU006A08 of the Junta de Castilla y León.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherTaylor & Francises
dc.relation.ispartofInternational Journal of Computer Mathematics. 2009, V. 86, n. 10-11, p. 1769-1777en
dc.rightsAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectUnsupervised learningen
dc.subjectExploratory projection pursuiten
dc.subjectBlack-box modelsen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherMatemáticases
dc.subject.otherMathematicsen
dc.titleThe application of a two-step AI model to an automated pneumatic drilling processen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1080/00207160802676612es
dc.identifier.doi10.1080/00207160802676612
dc.identifier.essn1029-0265
dc.journal.titleInternational Journal of Computer Mathematicsen
dc.volume.number86es
dc.issue.number10-11es
dc.page.initial1769es
dc.page.final1777es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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