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

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.

MoreLess

Year of publication:	2004
Authors:	Linker, Raphael ; Seginer, Ido
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 65.2004, 1, p. 19-29
Publisher:	Elsevier
Subject:	Database \| Extrapolation \| Prior knowledge \| Radial basis function \| Training domain

Online Resource

Check full text access |

More access options

Check Google Scholar

In libraries world-wide (WorldCat)

In German libraries (KVK)

subito order

I need help

More details

Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010748955