Gear predictor of manual transmission vehicles based on artificial neural network

WIT Transactions on Modelling and Simulation

Volumn 48, Issue , 2009, Pages 37-46

  Wefky, A M ^a   Espinosa, F ^a   Mazo, M ^a   Jimenez J A ^a   Santiso, E ^a   Gardel, A ^a   Perez D ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Feed forward artificial neural networks; Gear predictor; Manual transmission

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DRIVER BEHAVIOUR; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; GEAR RATIOS; LINEAR VELOCITY; MANUAL TRANSMISSIONS; MECHANICAL SYSTEMS; MODEL PARAMETERS; NETWORK INPUTS; NON-LINEAR RELATIONSHIPS; ROAD CONDITION; TRANSMIT POWER; UNIVERSAL MODEL;

AUTOMOBILE ENGINES; BACKPROPAGATION; COMPUTATIONAL METHODS; MACHINERY; MAGNETIC STORAGE; VEHICLE TRANSMISSIONS; VEHICLES;

NEURAL NETWORKS;

EID: 71749121613     PISSN: 1743355X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2495/CMEM090041     Document Type: Conference Paper

References (18)

1. Norman P. M., Gerald L., Charles W. Battle, Edward C.J, Understanding Automotive Electronics, 2003, Elsevier Science, USA.
2. http://en.wikipedia.org/wiki/Main.
3. Y. Bodyanskiy, S. Popov, Neural network approach to forecasting of quasiperiodic financial time series, European Journal of Operational Research, 175(3) (2006) 1357-1366.
4. M. Frize, C.M. Ennett, M. Stevenson, and H.C.E. Trigg, Clinical decision support systems for intensive care units: using artificial neural networks, Medical Engineering & Physics 23(3) (2001) 217-225.
5. M. Sloleimani-Mohseni, B. Thomas, Per Fahlen, Estimation of operative temperature in buildings using artificial neural networks, Journal of Energy and Buildings, 38 (2006) 635-640.
6. Y.J. Chen, Y.M. Chen, C.B. Wang, H.C. Chu, T.N. Tsai, Developing a multi-layer reference design retrieval technology for knowledge management in engineering design, Expert Systems with Applications, 29(4) (2005) 839-866.
7. S. Haykin, Neural Networks: a comprehensive foundation. New York: MacMillan College Publishing Company 1994. O. Nolles, Nonlinear system identification: from classical approaches to neural networks and fuzzy models, Spring-Verlag, Berlin, 2001.
8. Mukesh Khare, S.M. Shiva Nagendra, Artificial Neural Networks in Vehicular Pollution Modelling, Springer 2007
9. T. I. Maris, L. Ekonomou, G.P. Fotis, A. Nakulas, E. Zoulias, Electromagnetic field identification using artificial neural networks, Proceedings of the 8th WSEAS International Conference on Neural Networks, Vancouver, British Columbia, Canada, June 19-21, 2007.
10. Khalaf, A.A.M.; Abo-Eldahab, M.A.M.; Ali, M.M., System Modelling Using Neural Networks in the Presence of Noise, Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 10th IEEE International Conference on Volume 2, 14-17 Dec. 2003 Page(s):467-470 Vol.2.
11. Martin T. Hagan, Howard B. Demuth, Neural Network Design, 1996 by PWS Publishing Company.
12. Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford: Oxford University Press.
13. Sarle, W., 1997. Neural network frequently asked questions. ftp://ftp.sas.com/pub/neural/FAQ.html.U
14. Guang-Bin Huang; Lei Chen; Chee-Kheong Sien, Universal approximation using incremental constructive feedforward networks with random hidden nodes, Neural Networks, IEEE Transactions on Volume 17, Issue 4, July 2006 Page(s):879-892.
15. S.I. Tamura, M. Tateishi, Capabilities of a four-layered feedforward neural network: four layers versus three", IEEE Transactions on Neural Nets, 8(2) (1997) 251-255.
16. Sartori, M.A.; Antsaklis, P.J.; A simple method to derive bounds on the size and to train multilayer neural networks, Neural Networks, IEEE Transactions on Volume 2, Issue 4, July 1991 Page(s):467-471.
17. Huang, S.-C.; Huang, Y.-F.; Bounds on number of hidden neurons of multilayer perceptrons in classification and recognition; Circuits and Systems, 1990., IEEE International Symposium on 1-3 May 1990 Page(s):2500-2503 vol.4.
18. Guang-Bin Huang; Babri, H.A.; Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions, Neural Networks, IEEE Transactions on Volume 9, Issue 1, Jan. 1998 Page(s):224-229.