A task decomposition neural network approach to non- destructive testing problems World Congress on neural networks, San Diego, California (United States), Vol. 1, pp. 1,566-1.571. Lawrence Erlbaum Association (1994). ISBN 080581745X An artificial neural network technique to treat inverse problems, typically encountered in Non-Destructive Testing applications, is introduced. The basic architecture is proposed for an electrostatic test problem. We also address the case of plural defect recognition, and we also show how the method here described could be useful for more realistic NDT problems, commonly known as eddy current testing. Simulations show that our method can be very effective, particularly when a high accuracy of the identification procedure is required.

A task decomposition neural network approach to non-destructive testing problems

MORABITO, Francesco Carlo;Campolo M
1997

Abstract

A task decomposition neural network approach to non- destructive testing problems World Congress on neural networks, San Diego, California (United States), Vol. 1, pp. 1,566-1.571. Lawrence Erlbaum Association (1994). ISBN 080581745X An artificial neural network technique to treat inverse problems, typically encountered in Non-Destructive Testing applications, is introduced. The basic architecture is proposed for an electrostatic test problem. We also address the case of plural defect recognition, and we also show how the method here described could be useful for more realistic NDT problems, commonly known as eddy current testing. Simulations show that our method can be very effective, particularly when a high accuracy of the identification procedure is required.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/9623
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