DocumentCode
2635166
Title
Competitive learning algorithm for the fuzzy rule optimization
Author
Dai, Fengzhi ; Li, Long ; Kushida, Naoki ; Zhang, Baolong
Author_Institution
Coll. of Electron. Inf. & Autom., Tianjin Univ. of Sci. & Technol., Tianjin, China
fYear
2011
fDate
21-23 June 2011
Firstpage
779
Lastpage
783
Abstract
By merging the feed forward neural network, the competitive learning algorithm and the fuzzy control, the neural network-based adaptive fuzzy control algorithm is proposed. This system can produce more reasonable fuzzy rules by the competitive (clustering) algorithm, and control the object by the optimized fuzzy rules. The analysis of the system, the experimental result and considerations are given.
Keywords
feedforward neural nets; fuzzy set theory; optimisation; pattern clustering; clustering algorithm; competitive learning algorithm; feedforward neural network; fuzzy control; fuzzy rule optimization; neural network based adaptive fuzzy control algorithm; Algorithm design and analysis; Artificial neural networks; Clustering algorithms; Control systems; Fuzzy control; Guidelines; Training; adaptive vector quantization; competitive learning; fuzzy rule optimizing; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on
Conference_Location
Beijing
ISSN
pending
Print_ISBN
978-1-4244-8754-7
Electronic_ISBN
pending
Type
conf
DOI
10.1109/ICIEA.2011.5975691
Filename
5975691
Link To Document