Comparative analysis of multiple neural networks for online identification of a UAV

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 non-linear 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. © Springer-Verlag Berlin Heidelberg 2007.