DocumentCode
532816
Title
Notice of Retraction
Fault diagnosis based on hybrid algorithm of an improved adaptive GA and BP
Author
Tang Hongcheng ; Wang Ping ; Liu Chunxia
Author_Institution
Coll. of Autom. & Electron. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China
Volume
14
fYear
2010
fDate
22-24 Oct. 2010
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.
Although BP algorithm can adaptive change with the environment, it can not guarantee that it can find the global optimal solution every time. A hybrid method of an improved genetic algorithm and BP algorithm is proposed to overcome the disadvantages of BP algorithm and that the genetic algorithm is prone to premature. Fault diagnosis results prove that the proposed IAGA-BP method is accuracy and the proposed IAGA-BP method is more high accuracy and smaller error than other methods.
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.
Although BP algorithm can adaptive change with the environment, it can not guarantee that it can find the global optimal solution every time. A hybrid method of an improved genetic algorithm and BP algorithm is proposed to overcome the disadvantages of BP algorithm and that the genetic algorithm is prone to premature. Fault diagnosis results prove that the proposed IAGA-BP method is accuracy and the proposed IAGA-BP method is more high accuracy and smaller error than other methods.
Keywords
backpropagation; fault diagnosis; genetic algorithms; mechanical engineering computing; BP algorithm; IAGA-BP method; fault diagnosis; hybrid algorithm; improved adaptive GA; Cognition; Gallium; BP; Hybrid algorithm; Improved GA;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Type
conf
DOI
10.1109/ICCASM.2010.5622371
Filename
5622371
Link To Document