Full Text:
<3220>
CLC number: S625.5+1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 1
Clicked: 7007
Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks
| |||||||||||||
YU Chao-gang, YING Yi-bin, WANG Jian-ping, NOURAIN Jamal, YANG Jia. Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks[J]. Journal of Zhejiang University Science A, 2005, 6(4): 265-269.
@article{title="Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks",
author="YU Chao-gang, YING Yi-bin, WANG Jian-ping, NOURAIN Jamal, YANG Jia",
journal="Journal of Zhejiang University Science A",
volume="6",
number="4",
pages="265-269",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0265"
}
%0 Journal Article
%T Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks
%A YU Chao-gang
%A YING Yi-bin
%A WANG Jian-ping
%A NOURAIN Jamal
%A YANG Jia
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 4
%P 265-269
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0265
TY - JOUR
T1 - Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks
A1 - YU Chao-gang
A1 - YING Yi-bin
A1 - WANG Jian-ping
A1 - NOURAIN Jamal
A1 - YANG Jia
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 4
SP - 265
EP - 269
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0265
Abstract: Proportional integral plus feedforward (PI+FF) control was proposed for identifying the pipe temperature in hot water heating greenhouse. To get satisfying control result, ten coefficients must be adjusted properly. The data for training and testing the radial basic function (RBF) neural-networks model of greenhouse were collected in a 1028 m2 multi-span glasshouse. Based on this model, a method of coefficients adjustment is described in this article.
[1] Cai, X.Y., 2000. The Modern Vegetable Greenhouse Establishment and Management. Shanghai Science Technology Press, Shanghai (in Chinese).
[2] Chalabi, Z.S., Bailey, B.J., Wilkinson, D.J., 1996. A real-time optimal control algorithm for greenhouse heating. Computer and Electronics in Agriculture, 15:1-13.
[3] Chen, S., Cowan, C.F.N., Grant, P.M., 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans on Neural Networks, 2(2):302-309.
[4] Chen, S., Grant, P.M., Cowan, C.F.N., 1992. Orthogonal least-squares algorithm for training multioutput radial basis function networks. IEE Proceedings-F, 139(6):378-384.
[5] Davis, P.F., Hooper, A.W., 1991. Improve of greenhouse heating control. IEE Proceedings, 138(3):249-255.
[6] Frausto, H., Pieters, J., 2004. Modeling greenhouse temperature using system identification by means of neural networks. Neurocomputing, 56:243-248.
[7] Ferreira, P.M., Faria, E.A., Ruano, A.E., 2002. Neural network model in greenhouse air temperature prediction. Neurocomputing, 43:51-75.
[8] Linker, R., Seginer, I., 2004. Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models. Mathematics and Computers in Simulation, 65:19-29.
[9] Moody, J.E., Darken, C.I., 1988. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:282-294.
[10] Seginer, I., 1997. Some artificial neural network application to greenhouse environmental control. Computer and Electronics in Agriculture, 18:167-186.
Open peer comments: Debate/Discuss/Question/Opinion
<1>