The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles
AbstractIn 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%.
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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.
Göktas H, Çavusoğlu A, Şen B, Toktaş İ. The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles. Mathematical and Computational Applications. 2007; 12(2):87-96.Chicago/Turabian Style
Göktas, Haldun; Çavusoğlu, Abdullah; Şen, Baha; Toktaş, İhsan. 2007. "The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles." Math. Comput. Appl. 12, no. 2: 87-96.