Li, Yuhua, Pont, MJ, Parikh, CR and Jones, NB (2000) Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. In: SOFT COMPUTING TECHNIQUES AND APPLICATIONS. UNSPECIFIED. 6 pp. [Conference contribution]
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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%.
|Item Type:||Conference contribution (Paper)|
|Keywords:||engine misfire detection; neural networks; multi-layer Perceptron; radial basis function; condition monitoring; fault classification|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Intelligent Systems Research Centre
|Deposited By:||Dr Yuhua Li|
|Deposited On:||09 Mar 2010 16:13|
|Last Modified:||09 May 2016 10:51|
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