DocumentCode :
2468434
Title :
Dynamic engine modeling through linear programming Support Vector Regression
Author :
Lu, Zhao ; Sun, Jing ; Lee, Dongkyoung ; Butts, Ken
Author_Institution :
Electr. Eng. Dept., Tuskegee Univ., Tuskegee, AL, USA
fYear :
2009
fDate :
10-12 June 2009
Firstpage :
2070
Lastpage :
2075
Abstract :
In this paper, we develop a dynamic model for an internal combustion engine using Support Vector Regression (SVR). In particular, a linear programming SVR (LP-SVR) approach is investigated. The computational advantages and generalization capability of the LP-SVR dynamic engine model are illustrated through a case study, where a model is developed for an L4 gasoline engine. Simulation results are reported to demonstrate the effectiveness of proposed approach and to illustrate the trade-offs among different modeling attributes.
Keywords :
internal combustion engines; linear programming; mechanical engineering computing; regression analysis; support vector machines; L4 gasoline engine; dynamic engine modeling; internal combustion engine; linear programming; support vector regression; Automotive engineering; Calibration; Dynamic programming; Internal combustion engines; Linear programming; Petroleum; Statistical learning; Sun; Support vector machines; Vehicle dynamics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2009. ACC '09.
Conference_Location :
St. Louis, MO
ISSN :
0743-1619
Print_ISBN :
978-1-4244-4523-3
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2009.5160279
Filename :
5160279
Link To Document :
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