IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v65y2004i1p19-29.html
   My bibliography  Save this article

Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models

Author

Listed:
  • Linker, Raphael
  • Seginer, Ido

Abstract

Greenhouse operation and inside climate strongly depend on the outside weather. This implies that at least a year of data collection is required to cover the whole operational domain. Greenhouse-climate models calibrated with data limited to only a small region of the operating domain (weather and control), may therefore, produce erroneous predictions when applied to unfamiliar conditions.

Suggested Citation

  • Linker, Raphael & Seginer, Ido, 2004. "Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 19-29.
  • Handle: RePEc:eee:matcom:v:65:y:2004:i:1:p:19-29
    DOI: 10.1016/j.matcom.2003.09.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037847540300137X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2003.09.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Germán Díaz-Flórez & Jorge Mendiola-Santibañez & Luis Solís-Sánchez & Domingo Gómez-Meléndez & Ivan Terol-Villalobos & Hector Gutiérrez-Bañuelos & Ma. Araiza-Esquivel & Gustavo Espinoza-García & Juan , 2019. "Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber," Energies, MDPI, vol. 12(21), pages 1-22, October.
    2. Kisi, Özgür, 2008. "Constructing neural network sediment estimation models using a data-driven algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(1), pages 94-103.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:65:y:2004:i:1:p:19-29. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.