2010Unpublished venueRequires access

Comparative analysis of vehicle to pole collision models established using analytical methods and neural networks

Witold Pawlus, J.E. Nielsen, Hamid Reza Karimi, Kjell G. Robbersmyr

Open publisher page 14 citations

Abstract

This paper presents a comparison between two modeling approaches of vehicle to pole collision. Firstly, analytical and curve fitting methods are explained and subsequently they are utilized to create lumped parameter models. Having parameters of such systems and their responses we proceed to brief description of the radial basis function neural network and its application to the linear models' coefficients' identification. Comparative analysis of the models formulated according to those two different manners is done. (6 pages)

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What this paper is about

This paper presents a comparison between two modeling approaches of vehicle to pole collision. Firstly, analytical and curve fitting methods are explained and subsequently they are utilized to create lumped parameter models. Having parameters of such systems and their responses we proceed to brief description of the radial basis function neural network and its application to the linear models' coefficients' identification. Comparative analysis of the models formulated according to those two different manners is done. (6 pages)

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OpenAlex reports 14 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper presents a comparison between two modeling approaches of vehicle to pole collision. Firstly, analytical and curve fitting methods are explained and subsequently they are utilized to create lumped parameter models. Having parameters of such systems and their responses we proceed to brief description of the radial basis function neural network and its application to the linear models' coefficients' identification. Comparative analysis of the models formulated according to those two different manners is done. (6 pages)

Key concepts: Artificial neural network, Computer science, Collision, Radial basis function, Identification (biology), Basis (linear algebra), Function (biology), Curve fitting

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