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

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.