Neural network models in greenhouse air temperature prediction

Ferreira, P. M.; Faria, E. A.; Ruano, Antonio

http://hdl.handle.net/10400.1/2343

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Authors

Ferreira, P. M.

Faria, E. A.

Ruano, Antonio

Abstract(s)

The adequacy 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, is addressed. As the model is intended to be incorporated in an environmental control strategy botho--line and on-line methods could be of use to accomplish this task. In this paper known hybrid o--line training methods and on-line learning algorithms are analyzed. An o--line method and its application to on-line learning is proposed. It exploits the linear–non-linear structure found in radial basis function neural networks.