An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the “mixture of experts” type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a “classical” classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable – within – with that of the classical one, with efficiency of the order of . A possible hardware implementation of such a kind of neural architecture in future space missions is considered.

Cosmic Ray discrimination capabilities of ΔE-E silicon nuclear telescopes using neural network

Castellano, M.;
2000

Abstract

An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the “mixture of experts” type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a “classical” classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable – within – with that of the classical one, with efficiency of the order of . A possible hardware implementation of such a kind of neural architecture in future space missions is considered.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/11594
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