Abstract
This paper sumarises a comparative study of multiple neural networks as applied for the identification of the dynamics of an Unmanned Aerial Vehicle (UAV). Each of the networks are based on nonlinear autoregressive technique and are trained online. Variations in the architecture, batch size and the initial weights of the multi-network are analysed. A dynamic selection mechanism optimally chooses the most suitable output from the host of networks based on a selection criteria.
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© 2007 Springer-Verlag Berlin Heidelberg
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Puttige, V., Anavatti, S., Ray, T. (2007). Comparative Analysis of Multiple Neural Networks for Online Identification of a UAV. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_14
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DOI: https://doi.org/10.1007/978-3-540-76928-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
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