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
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.
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580529
