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Authors: Karima Amoura 1 ; Patrice Wira 2 and Said Djennoune 1

Affiliations: 1 Université Mouloud Mammeri, Algeria ; 2 Université de Haute Alsace, France

ISBN: 978-989-8425-84-3

Keyword(s): Artificial neural networks, Recurrent network, State space, State estimation, System identification, System dynamics.

Related Ontology Subjects/Areas/Topics: Adaptive Architectures and Mechanisms ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Complex Artificial Neural Network Based Systems and Dynamics ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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.

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Paper citation in several formats:
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

@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},
}

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

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