DocumentCode
551554
Title
A kind integrated adaptive fuzzy neural network tolerance analog circuit fault diagnosis method
Author
Qin, Xuefeng ; Han, Baoru ; Cui, Lei
Author_Institution
Dept. of Electron. Eng., Hainan Software Prof. Inst., Qionghai, China
Volume
1
fYear
2011
fDate
20-21 Aug. 2011
Firstpage
180
Lastpage
183
Abstract
Combining fuzzy theory and neural network is an effective way to be applied in fault diagnosis of analog circuit. For tolerance analog circuit fault, this paper proposed a kind new based on integrated adaptive fuzzy neural network the diagnosis method. The method first uses wavelet transform to extract the signal from the output sample, and characteristics of fault feature vectors are normalized. Then it uses the principal element analysis to reduce the fault sample dimension, the network architecture can be simplified, the computation complexity can be reduced. Afterward training and testing integrated adaptive fuzzy neural network with the preprocessed fault characteristic data. Experimentation indicates that the method has higher diagnosis nicety rate and effectively solves fault tolerance of ambiguity and problems.
Keywords
analogue circuits; electronic engineering computing; fault diagnosis; fault tolerance; fuzzy neural nets; fuzzy set theory; principal component analysis; wavelet transforms; adaptive fuzzy neural network tolerance; analog circuit fault diagnosis method; fuzzy theory; principal element analysis; wavelet transform; Adaptive systems; Analog circuits; Circuit faults; Fault diagnosis; Fuzzy neural networks; Training; Wavelet transforms; Fault diagnosis; Integrated adaptive fuzzy neural network; Principal Component Analysis; Wavelet Transform;
fLanguage
English
Publisher
ieee
Conference_Titel
Computing, Control and Industrial Engineering (CCIE), 2011 IEEE 2nd International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-9599-3
Type
conf
DOI
10.1109/CCIENG.2011.6007987
Filename
6007987
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