A comparative analysis of neural network performances in astronomical imaging Original Research Article

Neural networks are widely used as recognisers and classifiers since the second half of the 80ʹs; 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.