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Math. Comput. Appl. 2007, 12(2), 87-96; doi:10.3390/mca12020087

The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles

Gazi University, Technical Education Faculty, Ankara, Turkey
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Published: 1 August 2007
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Abstract

In this study, we have developed a platform which incorporates Artificial Neural Networks (ANNs) in simulating body dynamics of mobile ground vehicles (e.g. cars). This is a part of our research project in which we plan to provide a platform for educating the driver candidates in virtual environments: where the drivers can be educated fully in “Artificial Cities”. To start with, 6 different makes of cars with different engine properties has been simulated with the appropriate data provided by the manufacturers and rules of physics. A joystick steering wheel has been used to produce the necessary inputs for the ANN based physics engine. To train the network, Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function have been used. The statistical error levels are negligible. The Absolute Fraction of Variance (R2) values for both the training and test data are about 99.999% and the mean error value for both data group is lesser than 0.5%.
Keywords: Artificial Neural Networks; Driving Education; Car Simulation Artificial Neural Networks; Driving Education; Car Simulation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Göktas, H.; Çavusoğlu, A.; Şen, B.; Toktaş, İ. The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles. Math. Comput. Appl. 2007, 12, 87-96.

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Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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