AN EXPERIENCE WITH THE NEURAL NETWORK FOR AUTO-LANDING SYSTEM OF AN AIRCRAFT

Sreenatha G. Anavatti, Choi J. Young, Francois Pischery

Abstract

Generalization by the Neural Networks is an added advantage that can provide very good robustness and disturbance rejection properties. By providing a sufficient number of training samples (inputs and their corresponding outputs), a network can deal with some inputs it has never seen before. This ability makes them very interesting for control applications because not only they can learn complicated control functions but they are able to respond to changing or unexpected environments. Aircraft landing system provides one such scenario wherein the flight conditions change quite dramatically over the path of descent. The present work discusses the training of a neural network to imitate a robust controller for auto-landing of an aircraft. The comparisons with the robust controller indicate the additional advantages of the neural network

References

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Paper Citation


in Harvard Style

G. Anavatti S., J. Young C. and Pischery F. (2004). AN EXPERIENCE WITH THE NEURAL NETWORK FOR AUTO-LANDING SYSTEM OF AN AIRCRAFT . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 393-400. DOI: 10.5220/0002627603930400


in Bibtex Style

@conference{iceis04,
author={Sreenatha G. Anavatti and Choi J. Young and Francois Pischery},
title={AN EXPERIENCE WITH THE NEURAL NETWORK FOR AUTO-LANDING SYSTEM OF AN AIRCRAFT},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={393-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002627603930400},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AN EXPERIENCE WITH THE NEURAL NETWORK FOR AUTO-LANDING SYSTEM OF AN AIRCRAFT
SN - 972-8865-00-7
AU - G. Anavatti S.
AU - J. Young C.
AU - Pischery F.
PY - 2004
SP - 393
EP - 400
DO - 10.5220/0002627603930400