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A Comparative Analysis of Neural Network Performances in Astronomical Imaging by Rossella Cancelliere; Mario Gai is a Engineering article available to read on EtoBox.

What is A Comparative Analysis of Neural Network Performances in Astronomical Imaging about?

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.

Who reads A Comparative Analysis of Neural Network Performances in Astronomical Imaging?

Author

Rossella Cancelliere; Mario Gai

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0168-9274)

Published

2003

Language

EN

Field

Engineering (Physical Sciences)