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Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction
Wong K.I.; Vong C.M.; Wong P.K.; Luo J.
2015-02-03
Source PublicationNeurocomputing
ISSN18728286 09252312
Volume149Issue:Part APages:397-404
Abstract

Biofuels are important for the reduction of engine exhaust emissions and fossil fuel consumption. To use different blends of biofuels, the electronic control unit (ECU) of the engine must be modified and calibrated. However, the calibration process of ECU is very costly and time-consuming. Therefore, most of the engines can only use one specific biofuel blend; otherwise the engines cannot run properly. To alleviate this problem, a mathematical engine model can be used for predicting the engine performance at different ECU settings and biofuel blends so that the ECU can be re-calibrated in real-time via some controllers. The prediction of the engine model must be very fast and accurate for such online control purpose. It must also be very compact due to the limited memory size of the ECU. As a result, a new method called sparse Bayesian extreme learning machine (SBELM) is proposed in this paper to fulfill these requirements of the mathematical engine model for fast engine performance prediction and ECU online re-calibration. Experiments were conducted to compare SBELM with conventional ELM, Bayesian ELM (BELM) and back-propagated neural network (BPNN). Evaluation results show that SBELM can perform at least similar to, but mostly better than, ELM, BELM and BPNN, in terms of prediction accuracy. In terms of execution time, model size, and insensitivity to hidden neuron number, SBELM completely outperforms the other three methods. By these results, SBELM is verified to better fulfill the practical requirements of mathematical engine model for online engine performance prediction.

KeywordBiofuel Dual-fuel Engine Engine Performance Extreme Learning Machine Sparse Bayesian
DOI10.1016/j.neucom.2013.09.074
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000360028800045
Fulltext Access
Citation statistics
Cited Times [WOS]:27   [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 K.I.,Vong C.M.,Wong P.K.,et al. Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction[J]. Neurocomputing,2015,149(Part A):397-404.
APA Wong K.I.,Vong C.M.,Wong P.K.,&Luo J..(2015).Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction.Neurocomputing,149(Part A),397-404.
MLA Wong K.I.,et al."Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction".Neurocomputing 149.Part A(2015):397-404.
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