TY - GEN
T1 - Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection
AU - Li, Yuhua
AU - Pont, MJ
AU - Parikh, CR
AU - Jones, NB
N1 - Workshop 99 on Recent Advances in Soft Computing, LEICESTER, ENGLAND, JUL 01-02, 1999
PY - 2000
Y1 - 2000
N2 - 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%.
AB - 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%.
KW - engine misfire detection
KW - neural networks
KW - multi-layer Perceptron
KW - radial basis function
KW - condition monitoring
KW - fault classification
M3 - Conference contribution
T3 - ADVANCES IN SOFT COMPUTING
SP - 46
EP - 51
BT - Unknown Host Publication
T2 - SOFT COMPUTING TECHNIQUES AND APPLICATIONS
Y2 - 1 January 2000
ER -