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EnglishAn 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.
| Autori: | ||
| Titolo: | Cosmic Ray discrimination capabilities of ΔE-E silicon nuclear telescopes using neural network | |
| Rivista: | ||
| Data di pubblicazione: | 2000 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/S0168-9002(99)00926-2 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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