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Proceedings Paper

Using backpropagation to reckon with discrete and continuous signals from a silicon calorimeter
Author(s): Gianfranco Basti; Patrizia Castiglione; Marco Casolino; Antonio Luigi Perrone; Piergiorgio Picozza; Aldo Morselli
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Paper Abstract

We want to present a further development of our technique of backpropagation with stochastic preprocessing to recognize particle tracks in a silicon calorimeter on a satellite to detect cosmic ray composition. In the first release we applied our technique to distinguish between two classes of discrete patterns. In the present release we developed the stochastic preprocessing to deal with continuous patterns such as the energy deposited by a cosmic particle. From the theoretical standpoint we demonstrate that by such a preprocessing technique the neural net is able to represent the complexity of learning set in a polynomial and not exponential time. This work is a part of `Skynnet' international project supported by INFN (National Institute for Nuclear Physics) and partially devoted to the application of neural techniques for recognition of high energy particle tracks in spatial environment.

Paper Details

Date Published: 2 March 1994
PDF: 12 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169998
Show Author Affiliations
Gianfranco Basti, Pontifical Gregorian Univ. and INFN (Italy)
Patrizia Castiglione, Univ. di Roma La Sapienza (Italy)
Marco Casolino, Univ. di Roma Tor Vergata and INFN (Italy)
Antonio Luigi Perrone, Univ. di Roma Tor Vergata and INFN (Italy)
Piergiorgio Picozza, Univ. di Roma Tor Vergata and INFN (Italy)
Aldo Morselli, Univ. di Roma Tor Vergata (Italy)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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