ON-LINE MODELLING AND FORECASTING OF A MOVING CAR INFORMATION USING TD-HMLP NETWORK
DOI:
https://doi.org/10.11113/jt.v76.5884Keywords:
On-line modelling and forecasting, car speed, revolution, fuel balance in tank, injected fuel, trend data – hybrid multilayered perceptron networkAbstract
The study proposed a model called trend data hybrid multilayered perceptron network (TD-HMLP) coupled with a modified recursive prediction error (MRPE) training algorithm as a nonlinear modeling. An on-line model was used to forecast speed, revolution and fuel balanced in a Proton Gen2 car tank. The car measured the injected fuel from fuel injection sensor and become an input for the TD-HMLP model to forecast the speed, revolution and fuel balanced in tank. These forecasted variables were also measured from the car sensors. The criterions for performances are based on the one step ahead forecasting (OSA), multi-step ahead forecasting (MSA) and adjusted R2. The forecasting result showed that TD-HMLP network is better than the conventional HMLP network to maintain higher value in adjusted R2 and produce better step in multi-step ahead forecasting. These preliminary results show that the proposed modeling approach is capable to be used as an on-line information forecaster of a moving car.
References
Saad, Z., Osman, M. K., and Mashor, M.Y., 2014. Modelling and Forecasting of Car Speed using Hybrid Multilayered Neural Network, Journal of Contemporary Engineering Sciences. 7(13): 603-610.
Saad, Z., Mashor, M. Y., 2013. Model Structure Selection for Speed Forecasting with Nonlinear Autoregressive with an Exogenoues Input. The 4th International Conference on Intelligent Systems, Modelling and Simulation (ISMS2013).
Mashor, M. Y., 2009. On-line Nonlinear Modelling and Forecasting of Streamflow Using Neural Network. International Journal of The Computer, The Internet and Management. 17(1): 44-54.
Anant Bhaskar, G., Parag, D., and Mukesh, S. 2014. Artificial Neural Networks for Internal Combustion Engine Performance and Emission Analysis. International Journal of Computer Applications. 87(6): 23-27
Taghavifar, H., Taghavifar, H., Mardani, A., and Mohebbi, A. 2014. Exhaust Emissions Prognostication for DI Diesel Group-Hole Injectors using a Supervised Artificial Neural Network Approach. Fuel. 125(1): 81-89.
Mashor, M. Y., 2000. Hybrid Multilayered Perceptron Networks. Int. Journal of Systems Science. 31(6): 771-785.
Chen, S., Cowan, C. F. N., Billings, S. A., and Grant, P. M. 1990. A Parallel Recursive Prediction Error Algorithm for Training Layered Neural Network. Int. Journal of Control. 51(6): 1215-122.
Yin, P., and Fan, X. 2001. Estimating R2 Shrinkage in Multiple Regression: A Comparison of Analytical Methods. The Journal of Experimental Education. 69(2): 203-2.
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