Modelling and Prediction of Automotive Engine Air-ratio Using Relevance Vector Machine

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Status	已發表Published
	Modelling and Prediction of Automotive Engine Air-ratio Using Relevance Vector Machine
	Wong, P.K.; Wong, H. C.; Vong, C. M.
	2012-12-01
Source Publication	Proceedings of the 12th International Conference on Control, Automation, Robotics and Vision (ICARCV 2012)
Publication Place	U.S.A.
Publisher	IEEE
Abstract	Fuel efficiency and pollution reduction relate closely to air-ratio (i.e. lambda) among all of the automotive engine control variables. Accurate lambda prediction is essential for effective lambda control. This paper presents an online sequential algorithm for relevance vector machine (RVM) to build a time-dependent RVM lambda function which can be continually updated whenever a sample is added to, or removed from, the training dataset. In order to evaluate the effectiveness of the online sequential algorithm, three lambda time series obtained from experiments under different engine operating conditions were employed. The prediction results under the online sequential algorithm over unseen cases were compared with those under decremental least-squares support vector machine. From the experiments, the online sequential RVM shows promising results and is superior to the typical online algorithm.
Keyword	relevance vector machine online sequential algorithm engine air-ratio time-series prediction
Language	英語English
The Source to Article	PB_Publication
PUB ID	9795
Document Type	Conference paper
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Recommended Citation GB/T 7714	Wong, P.K.,Wong, H. C.,Vong, C. M.. Modelling and Prediction of Automotive Engine Air-ratio Using Relevance Vector Machine[C], U.S.A.:IEEE, 2012.
APA	Wong, P.K.., Wong, H. C.., & Vong, C. M. (2012). Modelling and Prediction of Automotive Engine Air-ratio Using Relevance Vector Machine. Proceedings of the 12th International Conference on Control, Automation, Robotics and Vision (ICARCV 2012).

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