DocumentCode
1654814
Title
Notice of Retraction
Optimization method of fuzzy system based on genetic algorithm
Author
Cheng Fei
Author_Institution
Manage. Coll., Hangzhou Dianzi Univ., Hangzhou, China
Volume
1
fYear
2010
Firstpage
367
Lastpage
371
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.
Currently, there is an increasing interest to genetic fuzzy systems with its learning and optimizing capabilities. The performance of a genetic fuzzy system is most depended on the ability of finding the best optimized rule-set and the precision of fuzzy variable definition. In this paper, a new method of optimization learning and modifying for genetic fuzzy system and the arithmetic is introduced, and the performance of the method is displayed.
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.
Currently, there is an increasing interest to genetic fuzzy systems with its learning and optimizing capabilities. The performance of a genetic fuzzy system is most depended on the ability of finding the best optimized rule-set and the precision of fuzzy variable definition. In this paper, a new method of optimization learning and modifying for genetic fuzzy system and the arithmetic is introduced, and the performance of the method is displayed.
Keywords
fuzzy set theory; fuzzy systems; genetic algorithms; optimisation; fuzzy variable definition; genetic algorithm; genetic fuzzy system; optimization method; Niobium; Variable speed drives; fuzzy logic; genetic algorithm; structure design; system optimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Management Science (ICAMS), 2010 IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6931-4
Type
conf
DOI
10.1109/ICAMS.2010.5553134
Filename
5553134
Link To Document