This is done with the optimization of a suitable error functional, revealing the differences between measurements and theory. For example, in eddy current testing, the sample is analyzed with electromagnetic excitations and responses. Then, those signals take part in the inverse problem solution, expecting to reveal any defect. The ANN are now widely known as good solvers for highly non-linear problems. The most used, the feed-forward scheme, is effective for the representation of an underlying physical phenomenon if a large data set is available.
Experimental measurements are always sensitive and noisy, involving hard data analysis.
For these reasons, the obtained defect image appears to be fuzzy, approximate, or incomplete. In this presentation, an inverse electromagnetic problem is solved for the defect reconstruction by eddy current testing. Due to this aim, a neural competitive layer is employed in complement of the classic feed-forward scheme for the defect localization. The device is shown in Fig. 1. It consists of a probe, exciting and detector coils, scanning over a magnetic plate specimen including a defect with air properties.