The aim of this work is to propose and discuss a technique which allows for classifying the defects found in metallic components on the basis of a non-destructive Remote-Field Eddy-Current Technique experimental test (RFEC). To this aim, we propose to employ a Hopfield associative memory as a neural classifier. The performances of the proposed approach are evaluated on real-world data.
Non-destructive test by the Hopfield network / S., Barcherini; L., Cipiccia; M., Maggi; Fiori, Simone; P., Burrascano. - 6:(2000), pp. 381-386.
Non-destructive test by the Hopfield network
FIORI, Simone;
2000-01-01
Abstract
The aim of this work is to propose and discuss a technique which allows for classifying the defects found in metallic components on the basis of a non-destructive Remote-Field Eddy-Current Technique experimental test (RFEC). To this aim, we propose to employ a Hopfield associative memory as a neural classifier. The performances of the proposed approach are evaluated on real-world data.File in questo prodotto:
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