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19 August 1993 Automatic redefinition of the fuzzy membership function to deal with high fluctuating phenomena in neural nets
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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.
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Gianfranco Basti, Patrizia Castiglione, Marco Casolino, Antonio Luigi Perrone, and Piergiorgio Picozza "Automatic redefinition of the fuzzy membership function to deal with high fluctuating phenomena in neural nets", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152624
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