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Publicação
Neural network models in greenhouse air temperature prediction
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Radial basis functions Neural networks Greenhouse environmental control Modelling
Contexto Educativo
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.
Editora
Elsevier