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Modelling and Prediction of Automotive Engine Specific Fuel Consumption Using Support Vector Machines
Pak Kin WONG1; Hang Cheong WONG1; Chi Man VONG2
2012
Conference Name12th International Conference on Control, Automation, Robotics & Vision
Source Publication2012 12th International Conference on Control Automation Robotics & Vision (ICARCV)
Conference Date5-7th December 2012
Conference PlaceGuangzhou, China
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.

KeywordRelevance Vector Machine Online Sequential Algorithm Engine Air-ratio Time-series Prediction
DOIhttp://doi.org/10.1109/ICARCV.2012.6485407
Language英语
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Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Electromechanical Engineering University of Macau Macao
2.Department of Computer and Information Science University of Macau Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Pak Kin WONG,Hang Cheong WONG,Chi Man VONG. Modelling and Prediction of Automotive Engine Specific Fuel Consumption Using Support Vector Machines[C],2012.
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