Application of Radial Basis Function Neural Networks to a Greenhouse Inside Air Temperature Model

The problem of the adequacy of radial basis function neural networks to model the inside air temperature as a-function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed. This type of network is structurally simple and suitab...

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Bibliographic Details
Published inIFAC Proceedings Volumes Vol. 33; no. 19; pp. 137 - 142
Main Authors Ferreira, P.M., Faria, E.A., Ruano, A.E.B.
Format Journal Article
LanguageEnglish
Published 01.07.2000
Subjects
Greenhouse Environmental Control
Modeling
Neural Networks
Radial Basis Functions
Neural Networks
Modeling
Radial Basis Functions
Greenhouse Environmental Control
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Summary:The problem of the adequacy of radial basis function neural networks to model the inside air temperature as a-function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed. This type of network is structurally simple and suitable to be integrated in real-time greenhouse environmental control systems. Due to the time variability of the process, training methods with on-line adaptation capabilities are needed Three of such methods are analysed in terms of fitness and network size. The model-predictive outputs obtained showed very close fittings to the measured values.
ISSN:1474-6670
DOI:10.1016/S1474-6670(17)40902-5