Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2343
Título: Neural network models in greenhouse air temperature prediction
Autor: Ferreira, P. M.
Faria, E. A.
Ruano, A. E.
Palavras-chave: Radial basis functions
Neural networks
Greenhouse environmental control
Modelling
Data: 2002
Editora: Elsevier
Citação: Ferreira, P. M.; Faria, E. A.; Ruano, A. E. Neural network models in greenhouse air temperature prediction, Neurocomputing, 43, 1-4, 51-75, 2002.
Resumo: The 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.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2343
DOI: http://dx.doi.org/10.1016/S0925-2312(01)00620-8
ISSN: 0925-2312
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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