A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS

Karima Amoura, Patrice Wira, Said Djennoune

2011

Abstract

In this paper, a specific neural-based model for identification of dynamical nonlinear systems is proposed. This artificial neural network, called State-Space Neural Network (SSNN), is different from other existing neural networks. Indeed, it uses a state-space representation while being able to adapt and learn its parameters. These parameters are the neural weights which are intelligible or understandable. After learning, the SSNN therefore is able to provide a state-space model of the dynamical nonlinear system. Examples are presented which show the capability of the SSNN for identification of multivariate dynamical nonlinear systems.

Paper Citation

in Harvard Style

Amoura K., Wira P. and Djennoune S. (2011). A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 369-376. DOI: 10.5220/0003680503690376

in Bibtex Style

@conference{ncta11,
author={Karima Amoura and Patrice Wira and Said Djennoune},
title={A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003680503690376},
isbn={978-989-8425-84-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS
SN - 978-989-8425-84-3
AU - Amoura K.
AU - Wira P.
AU - Djennoune S.
PY - 2011
SP - 369
EP - 376
DO - 10.5220/0003680503690376