A multisegment artificial neural network (ANN) is proposed as an interpolation technique for the evaluation of the electromagnetic field diffracted at the edge of anisotropic impedance wedges under plane wave illumination at oblique incidence. Multisegmentation is needed as the high-frequency wedge diffracted field is characterized by a number of discontinuities at the shadow boundaries of the geometrical optics and surface wave fields. The proposed approach is applied, as a test case, to the problem of an anisotropic impedance right-angled wedge illuminated by a skewly incident plane wave. Some exact analytical solutions valid for specific surface impedance tensors are used to obtain numerical data for the ANN training phase as well as to show the interpolation capabilities of the implemented ANN. Nevertheless, the proposed ANN structure is general and can be trained with data obtained from other available solutions (analytical, perturbative, numerical) valid for more general wedge configurations, eventually leading to a single software tool encompassing all of them and providing accurate approximations of the wedge diffracted field in a relatively short time, comparable to that of a closed form analytical.
A general multisegment artificial neural network architecture for the efficient evaluation of electromagnetic plane-wave wedge diffraction
MANARA, GIULIANO;NEPA, PAOLO;
2007-01-01
Abstract
A multisegment artificial neural network (ANN) is proposed as an interpolation technique for the evaluation of the electromagnetic field diffracted at the edge of anisotropic impedance wedges under plane wave illumination at oblique incidence. Multisegmentation is needed as the high-frequency wedge diffracted field is characterized by a number of discontinuities at the shadow boundaries of the geometrical optics and surface wave fields. The proposed approach is applied, as a test case, to the problem of an anisotropic impedance right-angled wedge illuminated by a skewly incident plane wave. Some exact analytical solutions valid for specific surface impedance tensors are used to obtain numerical data for the ANN training phase as well as to show the interpolation capabilities of the implemented ANN. Nevertheless, the proposed ANN structure is general and can be trained with data obtained from other available solutions (analytical, perturbative, numerical) valid for more general wedge configurations, eventually leading to a single software tool encompassing all of them and providing accurate approximations of the wedge diffracted field in a relatively short time, comparable to that of a closed form analytical.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
