Title :
Non-destructive test by the Hopfield network
Author :
Barcherini, S. ; Cipiccia, L. ; Maggi, M. ; Fiori, S. ; Burrascano, P.
Author_Institution :
Dept. of Ind. Eng., Perugia Univ., Italy
Abstract :
The aim of the 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
Keywords :
Hopfield neural nets; content-addressable storage; eddy current testing; flaw detection; pattern classification; structural engineering computing; Hopfield associative memory; defects classification; metallic components; neural classifier; nondestructive remote-field eddy-current technique experimental test; Associative memory; Corrosion; Electronic mail; Industrial engineering; Microstructure; Nondestructive testing; Paints; Performance evaluation; Prototypes; Voltage;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859425
