By Topic

A kind integrated adaptive fuzzy neural network tolerance analog circuit fault diagnosis method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xuefeng Qin ; Dept. of Electron. Eng., Hainan Software Prof. Inst., Qionghai, China ; Baoru Han ; Lei Cui

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.

Published in:

Computing, Control and Industrial Engineering (CCIE), 2011 IEEE 2nd International Conference on  (Volume:1 )

Date of Conference:

20-21 Aug. 2011