DocumentCode
3096796
Title
Notice of Retraction
Three-phase SPWM inverter fault diagnosis based on optimized neural networks
Author
Fan Bo ; Niu Jiangchuang
Author_Institution
Missile Coll., Air Force Eng. Univ., Sanyuan, China
Volume
3
fYear
2011
fDate
8-9 Sept. 2011
Firstpage
331
Lastpage
335
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.
The problem that how to use hierarchical genetic algorithm to determine the structure and parameters of neural networks is studied. The two grade coding structure of the hierarchical genetic algorithm is utilized to solve the ancient problem that when optimize the neural networks´ structure, connection weights, threshold at the same time, the efficiency was low. Furthermore, we compare the networks´ capability that optimized by the improved hierarchical genetic algorithm with the ones optimized by other algorithm, and prove the algorithm´s credibility through the simulation. At last, the improved adaptive genetic algorithm is used in the fault diagnosis of three-phase SPWM inverter circuit, and the simulation result show the method is correct and applied.
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.
The problem that how to use hierarchical genetic algorithm to determine the structure and parameters of neural networks is studied. The two grade coding structure of the hierarchical genetic algorithm is utilized to solve the ancient problem that when optimize the neural networks´ structure, connection weights, threshold at the same time, the efficiency was low. Furthermore, we compare the networks´ capability that optimized by the improved hierarchical genetic algorithm with the ones optimized by other algorithm, and prove the algorithm´s credibility through the simulation. At last, the improved adaptive genetic algorithm is used in the fault diagnosis of three-phase SPWM inverter circuit, and the simulation result show the method is correct and applied.
Keywords
PWM invertors; fault diagnosis; genetic algorithms; neural nets; algorithm credibility; hierarchical genetic algorithm; network capability; optimized neural network; three-phase SPWM inverter fault diagnosis; two grade coding structure; Circuit faults; Encoding; Fault diagnosis; Genetic algorithms; Genetics; Insulated gate bipolar transistors; Inverters; hierarchical genetic algorithm; neural networks; three-phase SPWM inverter fault diagnosis;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Engineering and Automation Conference (PEAM), 2011 IEEE
Conference_Location
Wuhan
Print_ISBN
978-1-4244-9691-4
Type
conf
DOI
10.1109/PEAM.2011.6135105
Filename
6135105
Link To Document