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Keywords
(2)
Auto Regressive
Neural Network Model
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Modelling of tropical greenhouse temperature by auto regressive and neural network models
Modelling of tropical greenhouse temperature by auto regressive and neural network models,10.1016/j.biosystemseng.2007.11.009,Biosystems Engineering,S
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Modelling of tropical greenhouse temperature by auto regressive and neural network models
(
Citations: 5
)
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S. L. Patil
,
H. J. Tantau
,
V. M. Salokhe
Journal:
Biosystems Engineering - BIOSYST ENG
, vol. 99, no. 3, pp. 423-431, 2008
DOI:
10.1016/j.biosystemseng.2007.11.009
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Citation Context
(3)
...Heating is widely been used in many industries especially in food [1-3], manufacturing [4, 5] and agricultural [
6-8
]...
...In manufacturing, one of the uses of heating is to increase the temperature in a furnace [5] and heat as a main source to realize tropical greenhouse environment in agricultural [
8
]...
...Some models under system identification are state space model [4], transfer function model [4], linear model [5, 6,
8
], bilinear model [5, 8] and direct-dependent model [5]...
...Some models under system identification are state space model [4], transfer function model [4], linear model [5, 6, 8], bilinear model [5,
8
] and direct-dependent model [5]...
...Heat in tropical greenhouse Sysid 2008, [
8
]...
...Patil et. al [
8
] studied the heating process in a tropical greenhouse by applying system identification technique...
...Patil et. al. [
8
] modeled tropical greenhouse temperature by auto regressive and neural networks models...
Nurlaila Ismail
,
et al.
Models for heating process — A review
...ECENTLY, researchers have shown an increased interest in heating process [
1-11
]...
Nazurah Tajjudin
,
et al.
Model Predictive Control using ARX model for steam distillation essent...
...For this reason, linear modeling has been applied in many instances [
2-10
]...
Nurlaila Ismail
,
et al.
Noise variation test for ARX model of a heating process in essential o...
References
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Greenhouse climate control: An integrated approach
(
Citations: 39
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J. C. Bakker
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G. P. A. Bot
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H. Challa
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N. J. van de Braak
Journal:
European Journal of Operational Research - EJOR
, vol. 86, no. 3, pp. 592-592, 1995
Real-time parameter estimation of dynamic temperature models for greenhouse environmental control
(
Citations: 24
)
J. Boaventura Cunha
,
C. Couto
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A. E. B. Ruano
Journal:
Control Engineering Practice - CONTROL ENG PRACTICE
, vol. 5, no. 10, pp. 1473-1481, 1997
Microclimate and Air Exchange Rates in Greenhouses covered with Different Nets in the Humid Tropics
(
Citations: 9
)
Harmanto
,
H. J. Tantau
,
V. M. Salokhe
Journal:
Biosystems Engineering - BIOSYST ENG
, vol. 94, no. 2, pp. 239-253, 2006
Simulated response of NN based identification and predictive control of hydro plant
(
Citations: 8
)
Nand Kishor
,
S. P. Singh
Journal:
Expert Systems With Applications - ESWA
, vol. 32, no. 1, pp. 233-244, 2007
System identification: theory for the user
(
Citations: 5595
)
L. Ljung
Published in 1987.
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Citations
(5)
Models for heating process — A review
Nurlaila Ismail
,
Mohd Hezri Fazalul Rahiman
,
Mohd Nasir Taib
Conference:
International Colloquium on Signal Processing & Its Applications - CSPA
, 2011
Artificial neural network and time series models for predicting soil salt and water content
Ping Zou
,
Jingsong Yang
,
Jianrong Fu
,
Guangming Liu
,
Dongshun Li
Journal:
Agricultural Water Management - AGR WATER MANAGE
, vol. 97, no. 12, pp. 2009-2019, 2010
Model Predictive Control using ARX model for steam distillation essential oil extraction system
Nazurah Tajjudin
,
Nurlaila Ismail
,
M. H. F. Rahiman
,
M. N. Taib
Conference:
International Conference on Intelligent and Advanced Systems - ICIAS
, 2010
Noise variation test for ARX model of a heating process in essential oil extraction plant
Nurlaila Ismail
,
Mohd Hezri Fazalul Rahiman
,
Mohd Nasir Taib
Conference:
International Colloquium on Signal Processing & Its Applications - CSPA
, 2010
Thermal behaviour model identification for an office space using BMS data
G. Mustafaraj
,
J. Chen
,
G. Lowry
Journal:
Building Services Engineering Research & Technology - BUILD SERV ENG RES TECHNOL
, vol. 30, no. 4, pp. 329-341, 2009