A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

Yuhua Li, MJ Pont, NB Jones

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

Abstract

In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages577-592
Number of pages6
Publication statusPublished (in print/issue) - 1999
EventCONDITION MONITORING `99, PROCEEDINGS - SWANSA, WALES
Duration: 1 Jan 1999 → …

Conference

ConferenceCONDITION MONITORING `99, PROCEEDINGS
Period1/01/99 → …

Bibliographical note

International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999

Keywords

  • engine misfire detection
  • neural networks
  • multi-layer perception
  • radial basis function
  • condition monitoring
  • fault classification

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