DocumentCode
525837
Title
Notice of Retraction
Reliability evaluation based on RS-ANN
Author
Li Tian ; Ling-Chun Li ; Ming-long Zhou ; Hong-wei Wu
Author_Institution
Anhui Univ. of Technol. & Sci., Wuhu, China
Volume
1
fYear
2010
fDate
12-13 June 2010
Firstpage
6
Lastpage
9
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.
Considering the reduction ability of rough set theory and the classification ability of artificial neural network, a rough set-artificial neural network combinatorial reliability evaluation model is constructed. The model enjoys a better topological structure and greatly increased speed for learning. The practical application to reliability evaluation for the east-red 1002 track-tractor verifies that the model has comparably fast and accurate classification abilities.
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.
Considering the reduction ability of rough set theory and the classification ability of artificial neural network, a rough set-artificial neural network combinatorial reliability evaluation model is constructed. The model enjoys a better topological structure and greatly increased speed for learning. The practical application to reliability evaluation for the east-red 1002 track-tractor verifies that the model has comparably fast and accurate classification abilities.
Keywords
agricultural machinery; condition monitoring; neural nets; production engineering computing; reliability; rough set theory; artificial neural network; combinatorial reliability evaluation model; east-red 1002 track-tractor; rough set theory; Reliability theory; artificial neural network (ANN); reliability evaluation; rough set(RS);
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5543678
Filename
5543678
Link To Document