Predicting the greenhouse inside air temperature with RBF neural networks
| Data(s) |
13/02/2013
13/02/2013
2001
30/01/2013
|
|---|---|
| Identificador |
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. AUT: ARU00698; |
| Idioma(s) |
eng |
| Direitos |
restrictedAccess |
| Palavras-Chave | #Neural Networks #Greenhouse Environmental Control #Modelling #Radial Basis Functions #Prediction |
| Tipo |
conferenceObject |
| Resumo |
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. |
