INVERSION OF A SEMI-PHYSICAL ODE MODEL

Laurent Bourgois, Gilles Roussel, Mohammed Benjelloun

2007

Abstract

This study proposes to examine the design methodology and the performances of an inverse dynamic model by fusion of statistical training and deterministic modeling. We carry out an inverse semi-physical model using a recurrent neural network and illustrate it on a didactic example. This technique leads to the realization of a neural network inverse problem solver (NNIPS). In the first step, the network is designed by a discrete reverse-time state form of the direct model. The performances in terms of generalization, regularization and training effort are highlighted in comparison with the number of weights needed to estimate the neural network. Finally, some tests are carried out on a simple second order model, but we suggest the form of a dynamic system characterized by an ordinary differential equation (ODE) of an unspecified r order.

Paper Citation

in Harvard Style

Bourgois L., Roussel G. and Benjelloun M. (2007). INVERSION OF A SEMI-PHYSICAL ODE MODEL . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 364-371. DOI: 10.5220/0001641803640371

in Bibtex Style

@conference{icinco07,
author={Laurent Bourgois and Gilles Roussel and Mohammed Benjelloun},
title={INVERSION OF A SEMI-PHYSICAL ODE MODEL},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2007},
pages={364-371},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001641803640371},
isbn={978-972-8865-82-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - INVERSION OF A SEMI-PHYSICAL ODE MODEL
SN - 978-972-8865-82-5
AU - Bourgois L.
AU - Roussel G.
AU - Benjelloun M.
PY - 2007
SP - 364
EP - 371
DO - 10.5220/0001641803640371