Neuro-fuzzy modelling of a plant growth in a hydroponic greenhouse
This paper deals with the modeling of dry matter production in a hydroponic greenhouse. Identification techniques are applied for the modeling, based on fuzzy logic and B-spline neural networks, for two growth models. For the design of these models subtractive clustering, the ASMOD algorithm and gen...
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| Outros Autores: | , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2013
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.1/2257 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2257 |
| Resumo: | This paper deals with the modeling of dry matter production in a hydroponic greenhouse. Identification techniques are applied for the modeling, based on fuzzy logic and B-spline neural networks, for two growth models. For the design of these models subtractive clustering, the ASMOD algorithm and genetic programming are employed and compared. The developed approach has been successfully applied for the prediction of tomato growth. |
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