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Neural network models in greenhouse air temperature prediction

dc.contributor.authorFerreira, P. M.
dc.contributor.authorFaria, E. A.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2013-02-13T10:56:47Z
dc.date.available2013-02-13T10:56:47Z
dc.date.issued2002
dc.date.updated2013-01-27T23:33:49Z
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 botho--line and on-line methods could be of use to accomplish this task. In this paper known hybrid o--line training methods and on-line learning algorithms are analyzed. An o--line method and its application to on-line learning is proposed. It exploits the linear–non-linear 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 air temperature prediction, Neurocomputing, 43, 1-4, 51-75, 2002.por
dc.identifier.doihttp://dx.doi.org/10.1016/S0925-2312(01)00620-8
dc.identifier.issn0925-2312
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2343
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevierpor
dc.subjectRadial basis functionspor
dc.subjectNeural networkspor
dc.subjectGreenhouse environmental controlpor
dc.subjectModellingpor
dc.titleNeural network models in greenhouse air temperature predictionpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage75por
oaire.citation.issue1-4por
oaire.citation.startPage51por
oaire.citation.titleNeurocomputingpor
oaire.citation.volume43por
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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