Title: Modelling nonlinear vehicle dynamics with neural networks

Authors: Simon J. Rutherford, David J. Cole

Addresses: Department of Engineering, Cambridge University, Trumpington Street, Cambridge CB2 1PZ, UK. ' Department of Engineering, Cambridge University, Trumpington Street, Cambridge CB2 1PZ, UK

Abstract: The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network|s parallel modelling ability over a range of operating conditions.

Keywords: nonlinear modelling; vehicle modelling; neural networks; nonlinear vehicle dynamics; vehicle control; identification; neural network weight convergence; off-line neural network training; online neural network training; vehicle design.

DOI: 10.1504/IJVD.2010.034101

International Journal of Vehicle Design, 2010 Vol.53 No.4, pp.260 - 287

Published online: 12 Jul 2010 *

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