Incremental modelling of automotive engine performance using LS-SVM

2009 IEEE International Conference on Information and Automation, ICIA 2009

Volumn , Issue , 2009, Pages 1537-1540

  Vong, C M ^a   Wong, P K ^a   Zhang, R ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMOTIVE ENGINE; CONTROL PARAMETERS; ELECTRONIC CONTROL UNITS; ENGINE PERFORMANCE; ENGINE TORQUE; LEAST SQUARES SUPPORT VECTOR MACHINES; MACHINE LEARNING TECHNIQUES; NONLINEAR FUNCTIONS; OFF-LINE ALGORITHM; OUTPUT TORQUE; PRECISION PREDICTION; TORQUE FUNCTION; TRIAL-AND-ERROR METHOD;

AUTOMOBILE ENGINES; CONTROL SYSTEM ANALYSIS; DC MOTORS; DYNAMOMETERS; ELECTRONICS INDUSTRY; ENGINES; LEARNING ALGORITHMS; MACHINE DESIGN; POWER ELECTRONICS;

SUPPORT VECTOR MACHINES;

EID: 70449657892     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICINFA.2009.5205161     Document Type: Conference Paper

References (9)

1. S. Haykin, Neural Networks, 2nd edition, 1999.
2. J. Valyon, G. Horvath, "A sparse least squares support vector machine classifier", in Proc. of IEEE InternationalJoint Conference on Neural Networks, 2004, Volume 1, pp. 543-548.
3. H. Tang, S. Xue, R. Chen, T. Sato, "Online weighted LS-SVM for hysteretic structural system identification", Engineering Structures, October 2006, Volume 28, Issue 12, pp. 1728-1735.
4. st edition, 2003. McGraw Hill.
5. L. Zhang, B. Zhang. "Geometrical representation of McCulloch-Pitts neural model and its applications", IEEE Transactions on Neural Networks. Vol. 10, no. 4, 1999, pp. 925-929.
6. st edition, 2002. World Scientific.
7. C. Yang, W. Lee, S. Lee, "Learning of Kernel Functions in Support Vector Machines", in Proc. Of InternationalJoint Conference on Neural Networks, 2006, pp. 1150-1155.
8. G. Golub, C. Van Loan, Matrix computations. Baltimore (MD): Johns Hopkins University Press, 1989.
9. K. Pelckmans, J. Suykens, T. Van Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor, and J. Vandewalle, LS-SVMlab: a MATLAB/C toolbox for Least Squares Support Vector Machines. Available at http://www.esat.kuleuven.ac.be/ sista/lssvmlab.