Automatic redefinition of the fuzzy membership function to deal with high fluctuating phenomena in neural nets
Gianfranco Basti, Patrizia Castiglione, Marco Casolino, A. Perrone, Piergiorgio Picozza · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
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.