Y Li
Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection
Li, Y; Pont, MJ; Parikh, CR; Jones, NB
Authors
MJ Pont
CR Parikh
NB Jones
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%.
Citation
Li, Y., Pont, M., Parikh, C., & Jones, N. (1999, July). Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. Presented at Recent Advances in Soft Computing Techniques and Applications, Leicester, UK
Presentation Conference Type | Other |
---|---|
Conference Name | Recent Advances in Soft Computing Techniques and Applications |
Conference Location | Leicester, UK |
Start Date | Jul 1, 1999 |
End Date | Jul 2, 1999 |
Publication Date | Jul 1, 2000 |
Deposit Date | Jul 27, 2015 |
Series Title | ADVANCES IN SOFT COMPUTING |
Keywords | Engine misfire detection, neural networks, multi-layer perceptron, radial basis function, condition monitoring, fault classification |
Additional Information | Event Type : Conference |
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