Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6843
Title: Modelling and prediction of particulate matter, NOx, and performance of a diesel vehicle engine under rare data using relevance vector machine
Authors: Wong, KI
Wong, PK
Cheung, CS 
Issue Date: 2012
Publisher: Hindawi Publishing Corporation
Source: Journal of control science and engineering, v. 2012, 782095, p.1-9 How to cite?
Journal: Journal of control science and engineering 
Abstract: Traditionally, the performance maps and emissions of a diesel engine are obtained empirically through many testes on the dynamometers because no exact mathematical engine model exists. In the current literature, many artificial-neural-network- (ANN-) based approaches have been developed for diesel engine modelling. However, the drawbacks of ANN would make itself difficult to be put into some practices including multiple local minima, user burden on selection of optimal network structure, large training data size, and overfitting risk. To overcome the drawbacks, this paper proposes to apply one emerging technique, relevance vector machine (RVM), to model the diesel engine, and to predict the emissions and engine performance. With RVM, only a few experimental data sets can train the model due to the property of global optimal solution. In this study, the engine speed, load, and coolant temperature are used as the input parameters, while the brake thermal efficiency, brake-specific fuel consumption, concentrations of nitrogen oxides, and particulate matter are used as the output parameters. Experimental results show the model accuracy is fairly good even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.
URI: http://hdl.handle.net/10397/6843
ISSN: 1687-5249 (print)
1687-5257 (online)
DOI: 10.1155/2012/782095
Rights: Copyright © 2012 Ka In Wong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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