DocumentCode
526908
Title
Notice of Retraction
Judgment for quality of sintered ore based on neural network
Author
Tiejun Zhang ; Duo Chen
Author_Institution
Dept. of Comput. Sci. & Technol., Tangshan Coll., Tangshan, China
Volume
1
fYear
2010
fDate
10-11 July 2010
Firstpage
43
Lastpage
45
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.
Focusing on the problem in production practice of sintering process, a novel classifier based on BP learning algorithm is proposed for on-line quality inference of sintered ore. In order to speed up the convergence rate of BP learning algorithm, the learning algorithm with adaptive variable step-size is adopted. On the basis of the above work a quality prediction model is proposed in this paper. Experimental results indicate the higher prediction accuracy rate and better generalization ability of the model.
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.
Focusing on the problem in production practice of sintering process, a novel classifier based on BP learning algorithm is proposed for on-line quality inference of sintered ore. In order to speed up the convergence rate of BP learning algorithm, the learning algorithm with adaptive variable step-size is adopted. On the basis of the above work a quality prediction model is proposed in this paper. Experimental results indicate the higher prediction accuracy rate and better generalization ability of the model.
Keywords
backpropagation; inspection; minerals; neural nets; pattern classification; production engineering computing; quality management; sintering; BP learning algorithm; adaptive variable step-size; classifier; convergence rate; neural network; on-line quality inference; quality prediction model; sintered ore quality; Production; Testing; Training; adaptive variable step-size; neural network; pattern recognition; sintering;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial and Information Systems (IIS), 2010 2nd International Conference on
Conference_Location
Dalian
Print_ISBN
978-1-4244-7860-6
Type
conf
DOI
10.1109/INDUSIS.2010.5565917
Filename
5565917
Link To Document