Using Neural Networks to Modeling Vehicle Dynamics

Article Preview

Abstract:

This paper is the result of a research program which focused on the statistical dynamics of vehicles. Most of the inputs of man-machine-field system have a random variation, so a systemic and statistical analysis of vehicle dynamics is obvious. In our study, data were obtained by measuring the dynamic parameters of vehicles and engines. Testing program aimed to capture a large range of operating regimes. To analyze the data the authors have used neural networks. There was adopted a NNARX (Neural Network Auto-Regressive with eXogene inputs) model with 4 inputs, 5 hidden units and 1 output. It can be concluded that the development of mathematical modeling using non-linear neural network can ensure the desired accuracy, conveniently is obtained by increasing the number of neurons in the hidden laws.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

133-138

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] C.O. Ilie, Statistical Modeling of Vehicle Dynamics, Military Technical Academy, Bucharest, 2008, pp.7-19.

Google Scholar

[2] Ljung, L. System Identification. Department of Electrical Engineering, Linkoping University, Sweden, (1999).

Google Scholar

[3] F. Popescu, F. Enache, Training of RBF Neural Networks: a comparative overview, Mircea cel Batran, Naval Academy Publishing House, Scientific Bulletin, Constanta, (2013).

Google Scholar

[4] Magnus Nørgaard, Neural Network Based System Identification Toolbox, second version, Technical Report 00-E-891, Department of Automation, Technical University of Denmark, (2000).

Google Scholar