DocumentCode
1945910
Title
Notice of Retraction
Research on identification of coal and waste rock based on PCA and GA-ANN
Author
He Yaqun ; He Jingfeng ; Zhou Nianxin ; Chen Bo ; Liang Haonan
Author_Institution
Sch. of Chem. Eng. & Technol., China Univ. of Min. & Technol., Xuzhou, China
Volume
6
fYear
2010
fDate
9-11 July 2010
Firstpage
579
Lastpage
584
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.
When exploring identification of coal and waste rock, 17 characteristic parameters of gray-scale histogram and gray level co-occurrence matrix (GLCM) were chosen according to their differences in gray scale and texture. Then, the principal component analysis (PCA) algorithm was used to get principal components from all the parameters chosen above. The principal components were defined as the inputs of GA-ANN to identify the experimental samples of coal and waste rock. The identification rate reaches up to 100% through simulating experiments which proves the feasibility of the characteristic parameters and principal component analysis. Finally, by comparing the simulation results with BP neural network, the identification not only demonstrates the validity in extracting the principal components from the 17 parameters, but also shows that the GA-ANN algorithm is superior to the traditional BP neural network in pattern recognition. The application of PCA combined with GA-ANN provides a new method in intelligent identification for coal and waste rock.
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.
When exploring identification of coal and waste rock, 17 characteristic parameters of gray-scale histogram and gray level co-occurrence matrix (GLCM) were chosen according to their differences in gray scale and texture. Then, the principal component analysis (PCA) algorithm was used to get principal components from all the parameters chosen above. The principal components were defined as the inputs of GA-ANN to identify the experimental samples of coal and waste rock. The identification rate reaches up to 100% through simulating experiments which proves the feasibility of the characteristic parameters and principal component analysis. Finally, by comparing the simulation results with BP neural network, the identification not only demonstrates the validity in extracting the principal components from the 17 parameters, but also shows that the GA-ANN algorithm is superior to the traditional BP neural network in pattern recognition. The application of PCA combined with GA-ANN provides a new method in intelligent identification for coal and waste rock.
Keywords
backpropagation; coal; genetic algorithms; industrial waste; matrix algebra; neural nets; parameter estimation; pattern recognition; principal component analysis; rocks; BP neural network; GA-ANN; PCA; coal; gray level cooccurrence matrix; gray-scale histogram; parameter identification; pattern recognition; principal component analysis; waste rock; Computer languages; GA-ANN; GLCM; Gray-scale histogram; PCA; characteristic parameters;
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.2010.5564418
Filename
5564418
Link To Document