Neural network models in greenhouse air temperature prediction

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 contr...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 43; no. 1; pp. 51 - 75
Main Authors Ferreira, P.M., Faria, E.A., Ruano, A.E.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2002
Subjects
Greenhouse environmental control
Modelling
Neural networks
Radial basis functions
Radial basis functions
Modelling
Neural networks
Greenhouse environmental control
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Summary: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 both off-line and on-line methods could be of use to accomplish this task. In this paper known hybrid off-line training methods and on-line learning algorithms are analyzed. An off-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.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(01)00620-8