Geodesic
Function approximation of Seidel aberrations by a neural network
Bollettino della Unione matematica italiana, Série 8, 7B (2004) no. 3, pp. 687-696

Voir la notice de l'article provenant de la source Biblioteca Digitale Italiana di Matematica

This paper deals with the possibility of using a feedforward neural network to test the discrepancies between a real astronomical image and a predefined template. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with some conveniently chosen statistical moments, evaluated along the $\{x, y\}$ axes; in this way a parsimonious method is obtained that allows a really effective approach to Seidel aberration diagnostics. 
@article{BUMI_2004_8_7B_3_a9,
     author = {Cancelliere, Rossella and Gai, Mario},
     title = {Function approximation of {Seidel} aberrations by a neural network},
     journal = {Bollettino della Unione matematica italiana},
     pages = {687--696},
     publisher = {mathdoc},
     volume = {Ser. 8, 7B},
     number = {3},
     year = {2004},
     zbl = {1182.41017},
     mrnumber = {MR2101659},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/BUMI_2004_8_7B_3_a9/}
}
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Cancelliere, Rossella; Gai, Mario. Function approximation of Seidel aberrations by a neural network. Bollettino della Unione matematica italiana, Série 8, 7B (2004) no. 3, pp. 687-696. http://geodesic.mathdoc.fr/item/BUMI_2004_8_7B_3_a9/