Determining heating pipe temperature in greenhouse using proportional integral plus feedforward control and radial basic function neural-networks

Journal of Zhejiang University: Science

Volumn 6 A, Issue 4, 2005, Pages 265-269

  Yu, Chao Gang ^a   Ying, Yi Bin ^a   Wang, Jian Ping ^a   Nourain, Jamal ^a   Yang, Jia ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b Talimu University   (China)

Author keywords

Greenhouse; Neural networks; PI control; Temperature

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; HEAT PIPES; MATHEMATICAL MODELS; PROPORTIONAL CONTROL SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

HEATING PIPE TEMPERATURE; HOT WATER HEATING GREENHOUSE; MULTI SPAN GLASSHOUSE; PROPORTIONAL INTEGRAL PLUS FEEDFORWARD CONTROL;

GREENHOUSES;

EID: 17244364363     PISSN: 10093095     EISSN: None     Source Type: Journal    
DOI: 10.1631/jzus.2005.A0265     Document Type: Article

References (10)

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.P., 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.