Fault diagnosis on analog circuits based on Integrated Learning Method

Author

Pan-feng, Chen ; Bao-yin, Du ; Wen, Qin

Author_Institution

Mech. & Electr. Eng. Inst., Hebei Normal Univ. of Sci. & Technol., Qinhuangdao, China

Volume

1

fYear

2010

fDate

10-11 July 2010

Firstpage

144

Lastpage

147

Abstract

The Integrated Learning Method (ILM) uses multiple learners to solve the same problem, which can greatly improve the generalization ability of learning systems. To address the fault diagnosis on analog circuits, aiming at the shortcomings of diagnosis and model stability with single RBF neural network to diagnose faults of analog circuit system, the paper discussed method to improve model diagnosis accuracy with Bagging algorithm of ILM to integrated multiple neural networks. The experiment results show the adoption of this scheme can significantly improve the performance of neural network diagnostic model.

Keywords

analogue circuits; fault diagnosis; learning (artificial intelligence); radial basis function networks; Bagging algorithm; RBF neural network; analog circuit system; fault diagnosis; integrated learning method; model diagnosis accuracy; model stability; neural network diagnostic model; Circuit faults; Integrated circuit modeling; Bagging algorithm; RBF neural network; fault diagnosis of analog circuit; integrated learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial and Information Systems (IIS), 2010 2nd International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4244-7860-6

Type

conf

DOI

10.1109/INDUSIS.2010.5565891

Filename

5565891