Performance evaluation of feedforward neural networks for modeling a vehicle to pole central collision

Authors:
Witold Pawlus;Kjell G. Robbersmyr;Hamid Reza Karimi
Affiliations:
University of Agder, Faculty of Engineering and Science, Department of Engineering, Norway;University of Agder, Faculty of Engineering and Science, Department of Engineering, Norway;University of Agder, Faculty of Engineering and Science, Department of Engineering, Norway
Venue:
GEMESED'11 Proceedings of the 4th WSEAS international conference on Energy and development - environment - biomedicine
Year:
2011

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Abstract

Artificial Neural Networks (ANNs) have strong potential in modeling nonlinear systems. This paper presents application of a feedforward neural network which utilizes back-propagation learning algorithm, in the area of modeling a vehicle to pole central collision. Kinematics of a typical mid-size vehicle impacting a rigid pole is reproduced by the means of neural networks approach. Firstly, a network is trained with the appropriate data set (acceleration, velocity, and displacement) and subsequently it is tested and simulated. We also provide a comparison concerning the efficiency and performance of each ANN created in this research. It is judged which of them generates the most satisfactory output in the shortest time.