DocumentCode
2244000
Title
Notice of Retraction
A novel modeling method of wood moisture content for drying process
Author
Dong-Yan Zhang ; Liang-Kuan Zhu ; Wen-Fang Yin ; Hong-Jie Gui
Author_Institution
Dept. of Electro-Mech. Eng., Northeast Forestry Univ., Harbin, China
Volume
4
fYear
2010
fDate
11-14 July 2010
Firstpage
1920
Lastpage
1924
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.
In this paper, a novel wood moisture content prediction model is established via SVR (support vector regression) for drying process with severe nonlinear and coupling. The particle position and velocity of particle swarm optimization (PSO) algorithm is used to optimize the model parameters, so as to realize wood moisture content prediction. Simulation results of Quercus mongolica show that the PSO algorithm had good performance for optimizing SVM model parameters, the PSO-SVM model had well dynamic track and forecasting characteristics, and could predict wood moisture content in drying process accurately, which are very significant to schedule implementation and control of 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.
In this paper, a novel wood moisture content prediction model is established via SVR (support vector regression) for drying process with severe nonlinear and coupling. The particle position and velocity of particle swarm optimization (PSO) algorithm is used to optimize the model parameters, so as to realize wood moisture content prediction. Simulation results of Quercus mongolica show that the PSO algorithm had good performance for optimizing SVM model parameters, the PSO-SVM model had well dynamic track and forecasting characteristics, and could predict wood moisture content in drying process accurately, which are very significant to schedule implementation and control of wood drying process.
Keywords
drying; forecasting theory; moisture; particle swarm optimisation; regression analysis; support vector machines; wood processing; wood products; PSO algorithm; PSO-SVM model; Quercus mongolica; SVM model parameter; dynamic track characteristics; forecasting characteristics; particle position; particle swarm optimization; particle velocity; support vector regression; wood drying process; wood moisture content prediction; Atmospheric modeling; Kernel; Mathematical model; Moisture; Predictive models; Schedules; Support vector machines; Modeling; PSO (Particle Swarm Optimization); Prediction; SVM (Support Vector Machines); Wood moisture content;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-6526-2
Type
conf
DOI
10.1109/ICMLC.2010.5580529
Filename
5580529
Link To Document