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Proceedings Paper

Automatic redefinition of the fuzzy membership function to deal with high fluctuating phenomena in neural nets
Author(s): Gianfranco Basti; Patrizia Castiglione; Marco Casolino; Antonio Luigi Perrone; Piergiorgio Picozza
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Paper Abstract

Usually, to discriminate among particle tracks in high energy physics a set of discriminating parameters is used. To cope with the different particle behaviors these parameters are connected by the human observer with boolean operators. We tested successfully an automatic method for particle recognition using a stochastic method to pre-process the input to a back propagation algorithm. The test was made using raw experimental data of electrons and negative pions taken at CERN laboratories (Geneva). From the theoretical standpoint, the stochastic pre-processing of a back propagation algorithm can be interpreted as finding the optimal fuzzy membership function notwithstanding high fluctuating (noisy) input data.

Paper Details

Date Published: 19 August 1993
PDF: 14 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152624
Show Author Affiliations
Gianfranco Basti, Pontifical Gregorian Univ. (Italy)
Patrizia Castiglione, Univ. of Rome "La Sapienza" (Italy)
Marco Casolino, Univ. of Rome "Tor Vergata" (Italy)
Antonio Luigi Perrone, Univ. of Rome "Tor Vergata" (Italy)
Piergiorgio Picozza, Univ. of Rome "Tor Vergata" (Italy)


Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

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