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

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  • 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
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    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.

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