Modelling greenhouse temperature using system identification by means of neural networks-Reference-Cited by-同舟云学术

Modelling greenhouse temperature using system identification by means of neural networks

Published:2004-01 Issue: Volume:56 Page:423-428
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Uchida Frausto Hugo,Pieters Jan G.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference6 articles.

1. R. Dogniaux, M. Lemoine, R. Sneyers, Année-type moyenne pour le traitement de problèmes de captation d’énergie solaire, [Typical reference year for treatment of solar energy captation problems], Misc. sér. B, no 45, Institut Royal Météorologique de Belgique, Bruxelles, 1978.

2. System Identification Toolbox User's Guide;Ljung,2000

3. M. Nørgaard, Neural network based system identification toolbox, Technical Report, 00-E-891, Department of Automation, Technical University of Denmark, 2000.

4. Neural Networks for Modelling and Control of Dynamic Systems;Nørgaard,2000

5. Performances of greenhouses with the presence of condensation on cladding materials;Pieters;J. Agric. Eng. Res.,1997

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