Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2294
Título: Choice of RBF model structure for predicting greenhouse inside air temperature
Autor: Ferreira, P. M.
Ruano, A. E.
Palavras-chave: Neural Networks
Greenhouse Environmental Control
Model Validation
Radial Basis Functions
Temperature Prediction
Data: 2002
Editora: IFAC, Elsevier
Citação: Ferreira, P. M.; Ruano, A. E. Choice of RBF model structure for predicting greenhouse inside air temperature, Trabalho apresentado em World Congress, In Proceedings of the 15th IFAC World Congress, 2002, Barcelona, 2002.
Resumo: The application of the radial basis function neural network to greenhouse inside air temperature modelling has been previously investigated by the authors. 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. Several training and learning methods were compared and the application of the Levenberg-Marquardt optimisation method was found to be the best way to determine the neural network parameters. A second-order model structure previously selected in the context of dynamic temperature models identification, was used. The model is intended to be incorporated in a real-time predictive greenhouse environmental control strategy. It is now relevant to question if the model structure used so far, selected in a different modelling framework, is the most correct in some sense. In this paper the usefulness of correlation-based model validity tests is addressed in order to answer the question mentioned above.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2294
ISBN: 9783902661746
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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