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Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction
Wong P.-K.; Wong H.-C.; Vong C.-M.
2012-02-01
Source PublicationInternational Journal of Engine Research
ISSN14680874 20413149
Volume13Issue:1Pages:28-40
Abstract

Fuel efficiency and pollution reduction relate closely to air-ratio (i.e. lambda) control among all the engine control variables. Lambda indicates the amount that the actual available air-fuel ratio mixture differs from the stoichiometric air-fuel ratio of the fuel being used. Accurate lambda prediction is essential for effective lambda control. This paper employs an emerging online time-sequence incremental algorithm and proposes one novel online timesequence decremental algorithm based on least squares support vector machines (LS-SVMs) to continually update the built LS-SVM lambda function whenever a sample is added to, or removed from, the training dataset. Moreover, the online time-sequence algorithm can also significantly shorten the function updating time as compared with function retraining from scratch. In order to evaluate the effectiveness of this pair of online time-sequence algorithms, three lambda time series obtained from experiments under different operating conditions are employed. The prediction results of the online time-sequence algorithms over unseen cases are compared with those under classical LS-SVMs, typical decremental LS-SVMs, and neural networks. Experimental results show that the online time-sequence incremental and decremental LS-SVMs are superior to the other three typical methods. © Authors 2011.

KeywordAir Ratio Lambda Prediction Online Least Squares Support Vector Machines Time Sequence
DOI10.1177/1468087411420280
URLView the original
Indexed BySCI
Language英语
WOS Research AreaThermodynamics ; Engineering ; Transportation
WOS SubjectThermodynamics ; Engineering, Mechanical ; Transportation Science & Technology
WOS IDWOS:000300054500003
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Citation statistics
Cited Times [WOS]:14   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Wong P.-K.,Wong H.-C.,Vong C.-M.. Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction[J]. International Journal of Engine Research,2012,13(1):28-40.
APA Wong P.-K.,Wong H.-C.,&Vong C.-M..(2012).Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction.International Journal of Engine Research,13(1),28-40.
MLA Wong P.-K.,et al."Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction".International Journal of Engine Research 13.1(2012):28-40.
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