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A neural network PID autotuner

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dc.contributor.authorRuano, Antonio
dc.contributor.authorLima, João
dc.contributor.authorMamat, R.
dc.contributor.authorFleming, P. J.
dc.date.accessioned2013-02-13T09:05:31Z
dc.date.available2013-02-13T09:05:31Z
dc.date.issued1995
dc.date.updated2013-01-28T16:03:51Z
dc.description.abstractProportional, Integral and Derivative (PID) regulators are standard building blocks for industrial automation. Their popularity comes from their rebust performance and also from their functional simplicity. Whether because the plant is time-varying, or because of components ageing, these controllers need to be regularly retuned.pt_PT
dc.identifier.citationRuano, A. E.; Lima, J. M. G.; Mamat, R.; Fleming, P. J. A Neural Network PID Autotuner, Trabalho apresentado em Int. Conf. on Engineering Applications of Neural Networks (EANN’95), In Int. Conf. on Engineering Applications of Neural Networks (EANN’95), Helsinki, 1995.por
dc.identifier.otherAUT: ARU00698; JLI00543;
dc.identifier.urihttp://hdl.handle.net/10400.1/2325
dc.language.isoengpor
dc.peerreviewedyespor
dc.titleA neural network PID autotunerpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceHelsinkipor
oaire.citation.endPage170por
oaire.citation.startPage167por
oaire.citation.titleInternational Conference on Engineering Applications of Neural Networks (EANN’95)por
person.familyNameRuano
person.familyNameLima
person.givenNameAntonio
person.givenNameJoão
person.identifier.ciencia-idF319-672D-5416
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0003-3561-4267
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublicationd96029c0-7218-497d-978c-fc61b85d1fb2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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