Modelling and Prediction of Particulate Matter, NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine
Joint Authors
Wong, Ka In
Wong, Pak Kin
Cheung, Chun Shun
Source
Journal of Control Science and Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Wong, Ka In& Wong, Pak Kin& Cheung, Chun Shun. 2012. Modelling and Prediction of Particulate Matter, NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine. Journal of Control Science and Engineering،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-497588
Modern Language Association (MLA)
Wong, Ka In…[et al.]. Modelling and Prediction of Particulate Matter, NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine. Journal of Control Science and Engineering No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-497588
American Medical Association (AMA)
Wong, Ka In& Wong, Pak Kin& Cheung, Chun Shun. Modelling and Prediction of Particulate Matter, NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine. Journal of Control Science and Engineering. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-497588
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-497588