Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

References

• Aydogan, H., A.A. Altun, and A.E. Ozcelik. 2011. Performance analysis of a turbocharged diesel engine using biodiesel with back propagation artificial neural network. Energy Education Science and Technology Part A: Energy Science and Research no. 28 (1):459–468.
   Web of Science ®Google Scholar
• Babajimopoulos, A., D.N. Assanis, D.L. Flowers, et al. 2005. A fully coupled computational fluid dynamics and multi-zone model with detailed chemical kinetics for the simulation of premixed charge compression ignition engines. International Journal of Engine Research no. 6 (5):497–512.
   Google Scholar
• Bishop, C.M. 1995. Neural Networks for Pattern Recognition. New York: Oxford University Press.
   Google Scholar
• Canakci, M., A. Erdil, and E. Arcaklioglu. 2006. Performance and exhaust emissions of a biodiesel engine. Applied Energy no. 83 (6):594–605.
   Web of Science ®Google Scholar
• Ghobadian, B., H. Rahimi, A.M. Nikbakht, et al. 2009. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy no. 34 (4):976–982.
   Web of Science ®Google Scholar
• Hanzevack, Emil L., Theresa W. Long, Chris M. Atkinson, and Michael L. Traver. 1997. Virtual sensors for spark ignition engines using neural networks. Proceedings of the 1997 American Control Conference no. 1:669–673.
   Google Scholar
• Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. New Jersey: Prentice-Hall.
   Google Scholar
• Heywood, John B. 1988. Internal Combustion Engine Fundamentals. New York: McGraw-Hill.
   Google Scholar
• Hiroyasu, Hiroyuki, T. Kadota, and M. Arai. 1983. Development and use of a spray combustion modeling to predict diesel engine efficiency and pollutant emissions (part 1 combustion modeling). Bulletin of the JSME no. 26 (214):569–575.
   Google Scholar
• Ismail, H. Mohamed, H.K. Ng, C.W. Queck, and S. Gan. 2012. Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy no. 92:769–777.
   Web of Science ®Google Scholar
• Khalil, A., M.N. Almasri, M. McKee, and J.J. Kaluarachchi. 2005. Applicability of statistical learning algorithms in groundwater quality modeling. Water Resources Research no. 41 (5).
   PubMed Web of Science ®Google Scholar
• Lim, S.S., T. Vos, A.D. Flaxman, et al. 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet no. 380 (9859):2224–2260.
   PubMed Web of Science ®Google Scholar
• Najafi, G., B. Ghobadian, T.F. Yusaf, and H. Rahimi. 2007. Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid. American Journal of Applied Sciences no. 4 (10):759–767.
   Google Scholar
• Oguz, H., I. Saritas, and H.E. Baydan. 2010. Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications no. 37 (9):6579–6586.
   Web of Science ®Google Scholar
• Ozgur, T., G. Tuccar, M. Ozcanli, and K. Aydin. 2011. Prediction of emissions of a diesel engine fueled with soybean biodiesel using artificial neural networks. Energy Education Science and Technology Part A: Energy Science and Research no. 27 (2):301–312.
   Web of Science ®Google Scholar
• Pyle, D. 1999. Data Preparation for Data Mining. San Francisco: Morgan Kaufmann.
   Google Scholar
• Rakopoulos, C.D., K.A. Antonopoulos, and D.C. Rakopoulos. 2007. Development and application of multi-zone model for combustion and pollutants formation in direct injection diesel engine running with vegetable oil or its bio-diesel. Energy Conversion and Management no. 48 (7):1881–1901.
   Web of Science ®Google Scholar
• Seeger, M. 2004. Gaussian processes for machine learning. International Journal of Neural Systems no. 14 (2):69–106.
   PubMedGoogle Scholar
• Tipping, M.E. 2001. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research no. 1 (3):211–244.
   Web of Science ®Google Scholar
• Tipping, M.E., and A.C. Faul. 2003. Fast marginal likelihood maximisation for sparse Bayesian models. Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, January 3–6.
   Google Scholar
• Traver, Michael L., Richard J. Atkinson, and Chris M. Atkinson. 1999. Neural network-based diesel engine emissions prediction using in-cylinder combustion pressure. SAE Paper no. 1999-01-1532.
   Google Scholar
• Wong, P.K., L.M. Tam, K. Li, and C.M. Vong. 2010. Engine idle-speed system modelling and control optimization using artificial intelligence. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering no. 224 (1):55–72.
   Web of Science ®Google Scholar
• Wong, P.K., Q. Xu, C.M. Vong, and H.C. Wong. 2012. Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine. IEEE Transactions on Industrial Electronics no. 59 (4):1988–2001.
   Web of Science ®Google Scholar
• Yuan, J., K. Wang, T. Yu, and M. Fang. 2007. Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process. Engineering Applications of Artificial Intelligence no. 20 (7):970–979.
   Web of Science ®Google Scholar
• Yusaf, T.F., B.F. Yousif, and M.M. Elawad. 2011. Crude palm oil fuel for diesel-engines: Experimental and ANN simulation approaches. Energy no. 36 (8):4871–4878.
   Web of Science ®Google Scholar