NDLI: Modeling of a greenhouse temperature : comparison between multimodel and neural approaches

Content Provider	IEEE Xplore Digital Library
Author	Laribi, I. Homri, H. Mhiri, R.
Copyright Year	2006
Description	Author affiliation: Departement Genie physique et Instrumentation, I.N.S.A.T de Tunis (Laribi, I.; Homri, H.; Mhiri, R.)
Abstract	The aim of this paper is to discuss different possibilities of modeling the temperature evolution inside an agricultural greenhouse. Our analysis is exploring a wide data measurements representing different profiles of inputs, outputs and disturbances. Our study particularly considers the situation where the control of the greenhouse temperature needs the use of a heating system. Two approaches have been considered in the present work: The first one is based on the multimodel techniques which reduce the system complexity by using a set of several simple linear models. In the second approach we use the feed forward neural networks that allows elaborating a single black box model that can spread out to another variable and command. Finally we have made a synthesis resting on the comparison between these two approaches by testing the performance of each one with the same data measurements. This work shows that both approaches give satisfactory results, multimodel representation is more suitable to obtain different variables describing the internal state system and to develop appropriate controller. The neural network model is rather a black box model but it is easier to obtain and could well be used to simulate different output variables at the same time (temperature and humidity in our case)
Starting Page	399
Ending Page	404
File Size	5258892
Page Count	6
File Format	PDF
ISBN	1424404967
DOI	10.1109/ISIE.2006.295627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-09
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Control systems Neural networks Temperature control Heating Feeds Feedforward neural networks Control system synthesis Network synthesis Testing Humidity neural network Agricultural Greenhouse identification modeling multimodel approach
Content Type	Text
Resource Type	Article

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