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Authors: Ouyang Guang ; Lin Jun and Zhang Ping

Affiliation: Beihang University, China

ISBN: 978-989-758-173-1

Keyword(s): Unsteady aerodynamics, State-Space representation, Back-propagation neural network, Parameter identification, Model optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Hybrid Learning Algorithms ; Methodologies and Methods ; Model Selection ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper proposes a hybrid model which combines state-space representation and back-propagation neural network to describe the aircraft unsteady aerodynamic characteristics. Firstly, the state-space model is analysed and evaluated using wind-tunnel experimental data. Subsequently, back-propagation neural network is introduced and combined with state-space representation to form a hybrid model. In this hybrid model, the separation point model in state-space representation is reserved to describe the time delay of the unsteady aerodynamic responses, while the conventional polynomial model is replaced by back-propagation neural network to improve accuracy and universality. Finally, lift coefficient and pitch moment coefficient data from the wind-tunnel experiments are used to estimate the hybrid model. With high similarity to the wind-tunnel data, the hybrid model presented in this paper is proved to be accurate and effective for aircraft unsteady aerodynamic modeling.

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Paper citation in several formats:
Guang, O.; Jun, L. and Ping, Z. (2016). Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network.In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 232-239. DOI: 10.5220/0005691702320239

@conference{icpram16,
author={Ouyang Guang. and Lin Jun. and Zhang Ping.},
title={Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={232-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005691702320239},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network
SN - 978-989-758-173-1
AU - Guang, O.
AU - Jun, L.
AU - Ping, Z.
PY - 2016
SP - 232
EP - 239
DO - 10.5220/0005691702320239

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