Analog circuits fault diagnosis based on Adaptive Fuzzy Neural Network

Author

Zhihong, Zhao ; Guoxin, Xu ; Junling, Xiao ; Binbin, Liu

Author_Institution

ShenYang Artillery Acad., Shenyang

fYear

2008

fDate

2-4 July 2008

Firstpage

473

Lastpage

477

Abstract

This paper mainly presents an analog circuit fault diagnosis by adaptive fuzzy neural network. Combines fuzzy theory with BPNN(back propagation neural network), an integrated self-adaptive NN is developed based on the Takagi-Sugeno fuzzy system. The training of network weights and optimization of membership functions are conducted employing hybrid algorithms. Finally, single electrical source complementary symmetry power amplifying circuit is illustrated. The feasibility and validity of the method are validated by simulation testing.

Keywords

analogue circuits; circuit analysis computing; fault diagnosis; fuzzy neural nets; fuzzy set theory; Takagi-Sugeno fuzzy system; adaptive fuzzy neural network; analog circuits fault diagnosis; back propagation neural network; complementary symmetry power amplifying circuit; hybrid algorithms; single electrical source amplifying circuit; Adaptive systems; Analog circuits; Bismuth; Control systems; Electronic mail; Fault diagnosis; Fuzzy neural networks; Fuzzy systems; Neural networks; Takagi-Sugeno model; AnalogCircuits; Back Propagation Neural Network; Fault Diagnosis; Fuzzy Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2008. CCDC 2008. Chinese

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-1733-9

Electronic_ISBN

978-1-4244-1734-6

Type

conf

DOI

10.1109/CCDC.2008.4597355

Filename

4597355