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Predicting the greenhouse inside air temperature with RBF neural networks
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Autores
Orientador(es)
Resumo(s)
The application of the Radial Basis Function (RBF) Neural Network (NN) to
greenhouse inside air temperature modelling has been previously investigated (Ferreira et
al., 2000a). In those studies, the inside air temperature is modelled as a function of the inside
relative humidity and of the outside temperature and solar radiation. A second-order model
structure previously selected (Cunha et al., 1996) in the context of dynamic temperature
models identification, is used.
Descrição
Palavras-chave
Neural Networks Greenhouse Environmental Control Modelling Radial Basis Functions Prediction
Contexto Educativo
Citação
Ferreira, P. M.; Ruano, A. E. Predicting the Greenhouse Inside Air Temperature with RBF Neural Networks, Trabalho apresentado em 2nd IFAC-CIGR Workshop on Intelligent Control for Agricultural Applications (ICAA'2001), In 2nd IFAC-CIGR Workshop on Intelligent Control for Agricultural Applications (ICAA'2001), Bali, 2001.