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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| Other Authors: | , |
| Format: | article |
| Language: | eng |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.1/2343 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2343 |
| 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 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. |
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