Name
Title | ||
|---|---|---|
Modelling greenhouse temperature using system identification by means of neural networks |
Abstract | ||
|---|---|---|
An NNARX system is proposed for modelling the internal greenhouse temperature as a function of outside air temperature and humidity, global solar radiation and sky cloudiness. The models show a good performance over a complete year using only two training periods, 1 week in winter and one in September. Finding the balance between the number of neurons in the hidden layer of the NNARX system and the number of iterations for model training is found to play an important role in obtaining this good performance. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.neucom.2003.08.001 | Neurocomputing |
Keywords | Field | DocType |
Auto-regressive model,Recurrent neural network,Backpropagation | Recurrent neural network,Greenhouse,Artificial intelligence,System identification,Artificial neural network,Meteorology,Simulation,Humidity,Backpropagation,Outside air temperature,Cloud cover,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
56 | 0925-2312 | 5 |
PageRank | References | Authors |
0.71 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugo Uchida Frausto | 1 | 5 | 0.71 |
| Jan G. Pieters | 2 | 15 | 1.46 |