A comparative analysis of neural network performances in astronomical imaging

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Abstract

Neural networks are widely used as recognisers and classifiers since the second half of the 80's; this is related to their capability of solving a nonlinear approximation problem. A neural network achieves this result by training; this iterative procedure has very useful features like parallelism, robustness and easy implementation. The choice of the best neural network is often problem dependent; in literature, the most used are the radial and sigmoidal networks. In this paper we compare performances and properties of both when applied to a problem of aberration detection in astronomical imaging. Images are encoded using an innovative technique that associates each of them with its most convenient moments, evaluated along the {x,y} axes; in this way we obtain a parsimonious but effective method with respect to the usual pixel by pixel description. © 2002 IMACS. Published by Elsevier Science B.V. All rights reserved.

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Cancelliere, R., & Gai, M. (2003). A comparative analysis of neural network performances in astronomical imaging. In Applied Numerical Mathematics (Vol. 45, pp. 87–98). https://doi.org/10.1016/S0168-9274(02)00237-4

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