Digital system fault diagnosis based on BP neural network

Qichuan Tian, Quan Pan, Diguang Gao, Rucheng Han, Quanxue Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, a fault diagnosis strategy of digital system based on BP neural network is presented. After training the neural network can be used to diagnose faults of digital system. Considering tradition BP algorithm of neural network may make weights run into local minimum. A modified algorithm are developed using BP algorithm and applying Metropolis criterion. Finally, this fault diagnosis system has been used in a digital system, the simulation setup is fabricated and tested, and the test results are compared with simulation results. Using this algorithm the approximation error of neural network is very small, and it is an effective approach and efficient in learning. In a conclusion, BP neural network is a good way to complex digital system fault diagnosis.

Original languageEnglish
Title of host publicationProceedings of the Second International Symposium on Instrumentation Science and Technology
EditorsT. Jiubin, W. Xianfang, T. Jiubin, W. Xianfang
Pages1/697-1/701
StatePublished - 2002
EventProceedings of the second International Symposium on Instrumentation Science and Technology - Jinan, China
Duration: 18 Aug 200222 Aug 2002

Publication series

NameProceedings of the Second International Symposium on Instrumentation Science and Technology
Volume1

Conference

ConferenceProceedings of the second International Symposium on Instrumentation Science and Technology
Country/TerritoryChina
CityJinan
Period18/08/0222/08/02

Keywords

  • BP neural network
  • Digital system
  • Fault code
  • Fault diagnosis
  • Metropolis criterion

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