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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

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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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%.

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
ID Code:5984
Deposited By:Dr Yuhua Li
Deposited On:09 Mar 2010 16:13
Last Modified:09 Mar 2010 16:13

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