Greenhouse air temperature modelling with radial basis function neural networks
Results on the application 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, are presented. As the model is intended to be incorporated in an pred...
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| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2013
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| Texto completo: | http://hdl.handle.net/10400.1/2303 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2303 |
| Resumo: | Results on the application 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, are presented. As the model is intended to be incorporated in an predictive control strategy both off-line and on-line methods are important to accomplish this task. In this paper hybrid off-line training methods and on-line learning algorithms are analysed. Results from a previously presented off-line method and its application to on-line learning are also presented. It exploits the linear-nonlinear structure found in radial basis function neural networks. |
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