DocumentCode
1957854
Title
Notice of Retraction
Dynamic modeling of wood drying process based on SLSSVM
Author
Dongyan Zhang ; Jun Cao ; Liping Sun
Author_Institution
Dept. of Electro-Mech. Eng., Northeast Forestry Univ., Harbin, China
Volume
1
fYear
2010
fDate
9-11 July 2010
Firstpage
431
Lastpage
435
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.
Least Squares Support Vector Machines(LSSVM) regression principle and sparsity configuration were introduced. In this paper online dynamic modeling based on Sparse LSSVM(SLSSVM) was proposed for wood drying process with strong coupling and nonlinear characteristics. The sample data of Fraxinus mandshurica in the speed-down drying stage were gathered in the experiments of a downscaled industrial wood drying kiln. According to the actual needs of predictive control, an online model of drying process was established for online predicting wood moisture content. Results of simulation and comparison experiments showed that the SLSSVM online model updated learning data based on basic sparse method to rolling optimize model structure so as to predict next system output, and could reflect current state of wood drying process more effectively. The model had a high predict precision, strong generalization ability and simple structure, which could be further used in online predictive control of practical wood drying process.
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.
Least Squares Support Vector Machines(LSSVM) regression principle and sparsity configuration were introduced. In this paper online dynamic modeling based on Sparse LSSVM(SLSSVM) was proposed for wood drying process with strong coupling and nonlinear characteristics. The sample data of Fraxinus mandshurica in the speed-down drying stage were gathered in the experiments of a downscaled industrial wood drying kiln. According to the actual needs of predictive control, an online model of drying process was established for online predicting wood moisture content. Results of simulation and comparison experiments showed that the SLSSVM online model updated learning data based on basic sparse method to rolling optimize model structure so as to predict next system output, and could reflect current state of wood drying process more effectively. The model had a high predict precision, strong generalization ability and simple structure, which could be further used in online predictive control of practical wood drying process.
Keywords
drying; least squares approximations; production engineering computing; support vector machines; wood processing; Fraxinus mandshurica; SLSSVM; downscaled industrial wood drying kiln; dynamic modeling; least squares support vector machines; speed-down drying; wood drying process; Computational modeling; Equations; Mathematical model; Predictive models; Dynamic modeling; Online prediction; SLSSVM(sparse least-squares support vector machines); Wood drying;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5565025
Filename
5565025
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