Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction

International Journal of Engine Research

Volumn 13, Issue 1, 2012, Pages 28-40

  Wong, P K ^a   Wong, H C ^a   Vong, C M ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Air ratio; Lambda prediction; Online least squares support vector machines; Time sequence

Indexed keywords

AIR FUEL RATIOS; AIR RATIO; DECREMENTAL ALGORITHMS; ENGINE CONTROL; FUEL EFFICIENCY; INCREMENTAL ALGORITHM; LEAST SQUARES SUPPORT VECTOR MACHINES; OPERATING CONDITION; POLLUTION REDUCTION; TIME SEQUENCES; TRAINING DATASET;

ALGORITHMS; FORECASTING; FUELS; SUPPORT VECTOR MACHINES;

AIR POLLUTION CONTROL;

EID: 84858176325     PISSN: 14680874     EISSN: 20413149     Source Type: Journal    
DOI: 10.1177/1468087411420280     Document Type: Article

References (18)

1. Manzie, C., Palaniswami, M., and Watson, H. Gaussian networks for fuel injection control. Proc. IMechE, Part D: J. Automobile Engineering, 2001, 215(D10), 1053-1068. DOI: 10.1243/0954407011528617. (Pubitemid 34459495)
2. Manzie, C., Palaniswami, M., Ralph, D., Watson, H., and Yi, X. Model predictive control of a fuel injection system with a radial basis function network observer. J. Dyn. Sys. Measure. Contr., Trans. ASME, 2002, 124(4), 648-658. (Pubitemid 40001286)
3. Li, G. Y. Application on intelligent control and MATLAB to electronically controlled engines, 2007 (Publishing House of Electronics Industry, Beijing, China). In Chinese.
4. Rototest Research Institute. Performance measurements on chassis dynamometers, 2005, available from www.rototest.com.
5. More, A. and Deo, M. C. Forecasting wind with neural networks. Mar. Struct., 2003, 16, 35-49. (Pubitemid 36073109)
6. Han, M. and Wang, Y. Analysis and modeling of multivariate chaotic time series based on neural network. Expert Sys. Appl., 2009, 36, 1280-1290.
7. Celik, V. and Arcaklioglu, E. Performance maps of a diesel engine. App. Energy, 2005, 81, 247-259.
8. Hashemi, N. and Clark, N. N. Artificial neural network as a predictive tool for emissions from heavyduty diesel vehicles in Southern California. Int. J. Engine Res., 2007, 8(4), 321-336. (Pubitemid 47477072)
9. Wang, X., Zhang, H., Zhang, C., Cai, X., Wang, J., and Wang, J. Prediction of chaotic time series using LS-SVM with automatic parameter selection. In Proceedings of 6th IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies, Dalian, China, 5-8 September 2005 (IEEE Computer Society Press, Los Alamitos, California), pp. 962-965.
10. Haykin, S. Neural networks: A comprehensive foundation, edition 2, 1999 (Prentice Hall).
11. Valyon, J. and Horvath, G. A sparse least squares support vector machine classifier. In Proceedings of IEEE International Joint Conference on Neural Networks, Budapest, Hungary, 25-29 July 2004 (IEEE, Piscataway, New Jersey), vol. 1, pp. 543-548. (Pubitemid 40011795)
12. Suykens, J., De Brabanter, J., Lukas, L., and Vandewalle, J. Least squares support vector machines, edition 1, 2002 (World Scientific Press).
13. Shi, Z. and Han, M. Support vector echo-state machine for chaotic time-series prediction. IEEE Trans. Neural Networks, 2007, 18(2), 359-372. (Pubitemid 46522562)
14. Vong, C. M., Wong, P. K., and Zhang, R. Incremental modelling of automotive engine performance using LS-SVM. In Proceedings of 2009 IEEE International Conference on Information and Automation, Zhuhai/Macau, China, 22-25 June 2009 (IEEE, Piscataway, New Jersey), pp. 1537-1540.
15. Golub, G. and Van Loan, C. Matrix computations, 1989 (Johns Hopkins University Press, Baltimore, MD).
16. Suykens, J., Van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle, J. Weight least squares support vector machines: Robustness and sparse approximation. Neurocomputing, 2002, 48(1-4), 85-105. (Pubitemid 36221490)
17. Cauwenberghs, G. and Poggio, T. Incremental and decremental support vector machine learning. In Advances in neural information processing systems (Eds T. K. Leen, T. G. Dietterich, and V. Tresp), 2001, vol. 13, pp. 409-415 (MIT Press, Cambridge, MA).
18. Li, X., Cao, G., and Zhu, X. Modeling and control of PEMFC based on least squares support vector machines. Energy Conv. Manage., 2006, 47, 1032-1050. (Pubitemid 43132720)