Loading...
Publication
Genetic assisted selection of RBF model structures for greenhouse inside air temperature prediction
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 452.92 KB | Adobe PDF |
Advisor(s)
Abstract(s)
This paper presents results on the application of Multi-Objective Genetic Algorithms to the selection of Radial
Basis Function Neural Networks structures. The neural networks are to be incorporated in a real-time predictive greenhouse environmental control strategy, as' predictors of the inside air temperature. Previous research conducted by the authors modelled the inside air temperature, as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected in the context of dynamic temperature models identification was
used. Several training and learning methods were compared, and the application of the Levenberg-Frquardt optimisation method
was found to be the best way to determine the neural network parameters. The application of correlation-based model-validity tests revealed that the validity of such a second-order model structure could be manually improved after inspection of the
tests results. Both network performance and validity are certainly affected by the number of neurons, the input variables considered and the time delays used. As the number of alternatives is huge, Multi-Objective Genetic Algorithms are applied here to the selection of network inputs and number of neurons.
Description
Keywords
Citation
Ferreira, P. M.; Ruano, A. E.; Fonseca, C. M. Genetic assisted selection of RBF model structures for greenhouse inside air temperature prediction, Trabalho apresentado em Conference on Control Applications, In Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003. Istanbul, Turkey, 2003.