A comparative analysis of neural network performances in astronomical imaging

Authors:
Rossella Cancelliere;Mario Gai
Affiliations:
University of Torino, Dipartimento di Matematica, Via Carlo Alberto 10, Torino 10123, Italy;University of Torino, Dipartimento di Matematica, Via Carlo Alberto 10, Torino 10123, Italy
Venue:
Applied Numerical Mathematics
Year:
2003

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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.