DocumentCode
526363
Title
Notice of Retraction
The prediction of pulverized coal ignition property based on piecewise least squares support vector machine
Author
Chang Aiying ; Wu Tiejun ; Xin Bao
Author_Institution
State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China
Volume
6
fYear
2010
fDate
9-11 July 2010
Firstpage
251
Lastpage
254
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.
Aimed at the quantitative analysis of pulverized coal ignition temperature, this paper presents a piecewise least squares support vector machine modeling method, where several sub-models are created according to the burning characteristics of lignite, bituminous coal, lean coal and anthracite coal etc. and the parameters of each sub-model are optimized independently. By implementing the piecewise LSSVM and the global LSSVM on coal fuel samples obtained from certain company, we find that the piecewise LSSVM behaves better than the global LSSVM on mean-square error and correlation coefficient, etc.
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.
Aimed at the quantitative analysis of pulverized coal ignition temperature, this paper presents a piecewise least squares support vector machine modeling method, where several sub-models are created according to the burning characteristics of lignite, bituminous coal, lean coal and anthracite coal etc. and the parameters of each sub-model are optimized independently. By implementing the piecewise LSSVM and the global LSSVM on coal fuel samples obtained from certain company, we find that the piecewise LSSVM behaves better than the global LSSVM on mean-square error and correlation coefficient, etc.
Keywords
coal; least squares approximations; mining industry; support vector machines; anthracite coal; bituminous coal; burning characteristics; correlation coefficient; lean coal; lignite; mean-square error; piecewise least squares; pulverized coal ignition property; support vector machine; Art; Computational modeling; Computers; Fires; Heating; Ignition; Predictive models; blending coal; igniting temperature; least squares support vector machine; subsection model;
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.2010.5563669
Filename
5563669
Link To Document