DocumentCode :
2653944
Title :
Research of Electronic Equipment Fault Diagnosis Algorithm Based on RBF Neural Network
Author :
Yuan, Lei ; Zhao, Heming
Author_Institution :
Sch. of Electron. & Inf. Eng., Soochow Univ., Suzhou, China
fYear :
2011
fDate :
22-23 Oct. 2011
Firstpage :
75
Lastpage :
78
Abstract :
This paper proposes an algorithm of failure diagnosis for electronic device. This algorithm can train existed failure diagnosis parameter sample sets, analysis internal relationship of diagnosis parameters and obtain the final result for device diagnosis, which achieves diagnosis adaptation about further sample parameter of device failure diagnosis. The advantage of algorithm is to optimize training process and control result of empirical function due to considering the prediction accuracy and training time of RBF in the constructing process.
Keywords :
electronic engineering computing; electronic equipment testing; failure analysis; fault diagnosis; radial basis function networks; RBF neural network; electronic device failure diagnosis; electronic equipment fault diagnosis algorithm; failure diagnosis parameter sample sets; training process optimization; Artificial neural networks; Biological neural networks; Electronic equipment; Fault diagnosis; Neurons; Training; Vectors; Electronic Equipment; Fault Diagnosis; K means; LLS; RBFN;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence Information Processing and Trusted Computing (IPTC), 2011 2nd International Symposium on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-1130-5
Type :
conf
DOI :
10.1109/IPTC.2011.26
Filename :
6103540
Link To Document :
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