DocumentCode
525816
Title
Notice of Retraction
Study on neural network modeling of greenhouse environment based on partial least squares algorithm
Author
Bin Zhao ; Keqi Wang
Author_Institution
Coll. of Electromech. Eng., Northeast Forestry Univ., Harbin, China
Volume
1
fYear
2010
fDate
12-13 June 2010
Firstpage
89
Lastpage
92
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The greenhouse environment model is an important basis to make control strategies and optimize control method. If correlation among multi-variables of greenhouse environmental model exists, the accuracy of the model decreases. In this paper, utilize partial least squares (PLS) to extract principal components of data, adopt radial basis function neural network (RBFNN) to construct control model of greenhouse environment in northern region of china. And this model is compared with the Orthogonal Least Square (OLS) algorithm in performance. The results indicate that RBF network model of the greenhouse environment based on PLS has smaller network structure, and is superior to OLS algorithm in the approximation ability and generalization ability. The model has laid a good foundation for designing control scheme and structure of greenhouse environment.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The greenhouse environment model is an important basis to make control strategies and optimize control method. If correlation among multi-variables of greenhouse environmental model exists, the accuracy of the model decreases. In this paper, utilize partial least squares (PLS) to extract principal components of data, adopt radial basis function neural network (RBFNN) to construct control model of greenhouse environment in northern region of china. And this model is compared with the Orthogonal Least Square (OLS) algorithm in performance. The results indicate that RBF network model of the greenhouse environment based on PLS has smaller network structure, and is superior to OLS algorithm in the approximation ability and generalization ability. The model has laid a good foundation for designing control scheme and structure of greenhouse environment.
Keywords
greenhouses; least squares approximations; neurocontrollers; optimisation; radial basis function networks; control method optimization; greenhouse environment; orthogonal least square algorithm; partial least squares algorithm; principal data components; radial basis function neural network; Green products; Heating; PLS; facility agriculture; greenhouse environment; model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5543538
Filename
5543538
Link To Document