Modelo de predicción de microclima de invernaderos, caso de estudio: invernadero de orquídeas del jardín Botánico "Reinaldo Espinosa".
In the cultivation production under hothouse conditions it is important to optimize and to control the handling of the ambience, using dynamic models; the auto-regressive not linear models, based on earnings measurements, it presents a stage of work that allows to study complex and not linear system...
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| Natura: | bachelorThesis |
| Lingua: | spa |
| Pubblicazione: |
2013
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| Accesso online: | http://dspace.unl.edu.ec/jspui/handle/123456789/12223 |
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Aggiungi Tag | Riassunto: | In the cultivation production under hothouse conditions it is important to optimize and to control the handling of the ambience, using dynamic models; the auto-regressive not linear models, based on earnings measurements, it presents a stage of work that allows to study complex and not linear systems capable of predicting series of time, making use of the skill(technology) of artificial intelligence in particular Networks Neuronales (RN). In the present work one developed models neuronales auto-regressive to predict the behavior(manner) of the Temperature, the Relative Moisture and the CO2 inside the hothouse. As result of the creation of the Network Neuronal has that with the variables of Temperature, Relative Moisture and CO2 they are in yields of 99,578 %, 96,61 % and 98,29 % respectively. And in information of error of prediction with a value 0,45 %, all these results obtained them with reasonable calculation times. Keywords: greenhouse, ntstool, neural networks, data. |
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