Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection

Yuhua Li, MJ Pont, CR Parikh, NB Jones

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

Abstract

In this paper, we apply radial basis function networks (RBFN), multilayer Perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10%.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages46-51
Number of pages6
Publication statusPublished (in print/issue) - 2000
EventSOFT COMPUTING TECHNIQUES AND APPLICATIONS -
Duration: 1 Jan 2000 → …

Publication series

NameADVANCES IN SOFT COMPUTING

Conference

ConferenceSOFT COMPUTING TECHNIQUES AND APPLICATIONS
Period1/01/00 → …

Bibliographical note

Workshop 99 on Recent Advances in Soft Computing, LEICESTER, ENGLAND, JUL 01-02, 1999

Keywords

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

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