Li, Y, Pont, MJ, Parikh, CR and Jones, NB 2000, Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection , in: Recent Advances in Soft Computing Techniques and Applications, 1-2 July 1999, Leicester, UK.
Full text not available from this repository. (Request a copy)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 or Workshop Item (Paper) |
|---|---|
| Schools: | Schools > School of Computing, Science and Engineering |
| Journal or Publication Title: | Advances in Soft Computing |
| Publisher: | Physica-Verlag: Heidelberg |
| Refereed: | Yes |
| Series Name: | ADVANCES IN SOFT COMPUTING |
| Funders: | Non funded research |
| Depositing User: | Yuhua Li |
| Date Deposited: | 27 Jul 2015 16:55 |
| Last Modified: | 05 Apr 2016 18:18 |
| URI: | http://usir.salford.ac.uk/id/eprint/33143 |
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