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Neural network models in greenhouse environmental control

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dc.contributor.authorFerreira, P. M.
dc.contributor.authorFaria, E. A.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2013-02-05T14:11:20Z
dc.date.available2013-02-05T14:11:20Z
dc.date.issued2000
dc.date.updated2013-01-28T17:52:11Z
dc.description.abstractThe adequacy of radial basis function neural networks to model the inside air temperature of a hydroponic greenhouse as a function of the outside air temperature and solar radiation, and the inside relative humidity, is addressed. As the model is intended to be incorporated in an environmental control strategy both off-line and on-line methods could be of use to accomplish this task. In this paper hybrid off-line training methods and on-line learning algorithms are analysed. An off-line method and its application to on-line learning is presented. It exploits the linear-nonlinear structure found in radial basis function neural networks.por
dc.identifier.citationFerreira, P. M.; Faria, E. A.; Ruano, A. E. Neural network models in greenhouse environmental control, Trabalho apresentado em 6th Int. Conf. on Engineering Applications of Neural Networks (EANN 00), In 6th Int. Conf. on Engineering Applications of Neural Networks (EANN 00), Kingston Upon Thames, 2000.por
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2218
dc.language.isoengpor
dc.peerreviewedyespor
dc.titleNeural network models in greenhouse environmental controlpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceKingston Upon Thamespor
oaire.citation.endPage9por
oaire.citation.startPage1por
oaire.citation.title6th Int.ernational Conference on Engineering Applications of Neural Networks (EANN 00)por
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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