DocumentCode
2801139
Title
A fault identification approach for analog circuits using fuzzy neural network mixed with genetic algorithms
Author
Gechao, Liang ; Yigang, He
Author_Institution
Fac. of Electr. & Inf. Eng., Hunan Univ., Changsha, China
Volume
2
fYear
2003
fDate
8-13 Oct. 2003
Firstpage
1267
Abstract
A fault identification approach for nonlinear analogue systems is presented. A fuzzy neural network is developed based on the improving fuzzy weighted reasoning method. The training of network weights and optimization of membership functions are conducted employing genetic algorithms. Fuzzy rules can be realized through the refresh of the weights of the neural network. The availability of the method is examined by simulated test examples.
Keywords
analogue circuits; fault diagnosis; fuzzy neural nets; fuzzy set theory; genetic algorithms; learning (artificial intelligence); analog circuits; fault identification; fuzzy neural network; fuzzy rule; fuzzy weighted reasoning method; genetic algorithms; membership functions; nonlinear analogue systems; optimization; training; Analog circuits; Circuit faults; Circuit testing; Clustering algorithms; Fault diagnosis; Fuzzy neural networks; Fuzzy reasoning; Genetic algorithms; Neurons; Virtual manufacturing;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN
0-7803-7925-X
Type
conf
DOI
10.1109/RISSP.2003.1285774
Filename
1285774
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