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Proceedings Paper

Study and optimization of a neural network with fuzzy logic preprocessing for particle identification on a cosmic ray detector
Author(s): Marco Casolino; M. Candusso; M. P. DePascale; Aldo Morselli; Piergiorgio Picozza; M. Ricci
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Paper Abstract

A fuzzy logic preprocessing is used in connection with a back propagation neural network in particle recognition. As application on 4 GeV CERN experimental and Monte Carlo data of e- and (pi) (superscript -, taken with the prototype of the silicon Tungsten calorimeter of the Wizard collaboration, is shown. This preprocessing consists in giving as input to the net the membership value, for a given discriminating parameter value, to belong to a given particle class. In this way the input layer receives a normalized input. The net can then exploit the correlations between different parameters, resulting in an increased convergence speed and recognition capability of the net. Other advantages of this approach are its noise robustness and the simple generalization of other particle classes or energies.

Paper Details

Date Published: 13 June 1995
PDF: 9 pages
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211797
Show Author Affiliations
Marco Casolino, Univ. di Roma and INFN (Italy)
M. Candusso, Univ. di Roma and INFN (Italy)
M. P. DePascale, Univ. di Roma and INFN (Italy)
Aldo Morselli, Univ. di Roma and INFN (Italy)
Piergiorgio Picozza, Univ. di Roma and INFN (Italy)
M. Ricci, INFN Lab. Nazionali di Frascati (Italy)


Published in SPIE Proceedings Vol. 2493:
Applications of Fuzzy Logic Technology II
Bruno Bosacchi; James C. Bezdek, Editor(s)

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