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IEEE Transactions on Magnetics (1997), pp.

Wavelet Tools for Improving the Accuracy of Neural Network Solution of Electromagnetic Inverse Problems

Alessandro Formisano(a), Raffaele Martone(a) and Francesco Carlo Morabito(b)

(a) Dip. di Ingegneria dell’ Informazione, Seconda Università di Napoli, Via Roma 29, I-81131 Aversa (CE), ITALY
(b) Dip. di Ingegneria Elettronica e Matematica Applicata, Università di Reggio Calabria, Via E. Cuzzocrea 48, I-89127 Reggio Calabria, ITALY

A neural network model is proposed to treat inverse problems in electromagnetics, which includes wavelet functions to improve local approximation capabilities. This processor couples the advantages of an interpretation of the problem based on "features" to the accuracy derived from using wavelets where local corrections are needed. The combined model allows to cope with singularities of the mapping and to slightly modify the mapping in real time. The detection and characterization of a circular defect in a conducting plate by using eddy current testing is shown to take advantage from the proposed approach in a test case, when unforeseen disturbances are present.

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