Multi-factor predication of diesel engine by using artificial neural networks

International Journal of Digital Content Technology and its Applications

Volumn 4, Issue 6, 2010, Pages 161-167

  Xiao, Wenyong ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Artificial neural network; Engine predictive model; Non linear

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; DIESEL ENGINES; MODEL PREDICTIVE CONTROL; PREDICTIVE ANALYTICS;

MODEL BASED ADAPTIVE CONTROL; MULTI-STEP-AHEAD PREDICTIONS; NEURAL NETWORK MODEL; NON LINEAR; NON-LINEAR DYNAMICS; PREDICTIVE MODELING; SYSTEM UNCERTAINTIES; TIME-VARYING EFFECTS;

NEURAL NETWORKS;

EID: 78651547983     PISSN: 19759339     EISSN: None     Source Type: Journal    
DOI: 10.4156/jdcta.vol4.issue6.19     Document Type: Article

References (16)

1. Badreddine, B., Zaremba, A.,Sun, J., Lin, F., Active damping of engine idle speed oscillation by applying adaptive PID control. SAE Technical Paper No. 2001-01-0261. 2001.
2. Hrovat, D., Sun, J., Models and control methodologies for IC engine idle speed control design. Control Engineering Practice 5 (8), 93-10. 1997.
3. Howell, M.N., Best, M.C, On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata. Control Engineering Practice 8, 147-154, 2000.
4. Shim, D., Khargonekar, P.P., Ribbens, W., Idle speed control for automotive engine. Proceedings of the 1995 American Control Conference, Seattle, USA, 1995.
5. Wedeneker Mirosaw, Czarnigowski Jacek, Adaptive control of the idle conditions of spark ignition engine. SAE_NA Technical Paper SAE_NA 2003-01-15, 2003.
6. Chang, T.K., 2001. Process fault-tolerant control based on adaptive neural network. Ph.D. Thesis, Liverpool John Moores University, UK.
7. Girosi, F., Poggio, T., 1999. Network and the best approximation property. Biological Cybernetics 63, 169-176.
8. Zhi, T., Kristine, L., Bell, H.L., Van, T., 2001. A recursive least squares implementation for LCMP beam-forming under quadratic constraint. IEEE Transaction on Signal Processing 49 (6), 354-362.
9. De Jesus, O., Pukrittayakamee, A., Hagan, M.T., 2001. A comparison of neural network control algorithms. IEEE Transaction on Neural Network 12, 364-373.
10. Baydan, H. E. (2008). Determination of use of bioethanol, methyl ester and diesel fuel blends to engine performance in diesel engines. MS thesis, Selcuk University Graduate School of Natural and Applied Sciences, Department of Mechanical Education.
11. Canakci, M., Ozsezen, A. N., Arcaklioglu, E., & Erdil, A. (2009). Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil. Expert Systems with Applications, 36(5), 9268-9280.
12. Dincer, K., Tasdemir, S., Baskaya, S., & Uysal, B. Z. (2008). Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque Hilsch vortex tubes using artificial neural networks. Applied Thermal Engineering, 28(17-18), 2380-2390.
13. Kalogirou, S. A. (2001). Artificial neural networks in the renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews, 5, 373-401.
14. Yang, I. H., Yeo, M. S., & Kim, K. W. (2003). Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conversion and Management, 44(17), 2791-2809.
15. Nasr, G. E., & Badr, C. J. (2003). Back-propagation neural networks for modeling gasoline consumption. Energy Conversion and Management, 44(6), 893-905.
16. Kurt, H., Atik, K., Ozkaymak, M., & Binark, A. K. (2007). The artificial neural networks approach for evaluation of temperature and density profiles of salt gradient solar pond.Journal of the Energy Institute, 80 (1), 46-51.