Non-destructive test by the Hopfield network

S. Barcherini, L. Cipiccia, M. Maggi, S. Fiori, P. Burrascano
2000 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium  
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. Receiving winding with induced voltage
doi:10.1109/ijcnn.2000.859425 dblp:conf/ijcnn/BarcheriniCMFB00 fatcat:fv3vp7f6evemdmyz5jkwaun4z4