The computation of the field diffracted from an impedance wedge is of relevant importance in the solution of high-frequency radiation and scattering problems. Few analytically exact or approximate diffraction coefficients for impedance wedge scattering have been presented in the literature. They are relevant to specific electrical and geometrical wedge configurations, and some exact solutions are computationally intensive to compute. An artificial neural network (ANN) performing such a computation is presented, with the objective of improving the numerical efficiency of the field evaluation procedure and to obtain a single tool spanning all the different domains of the known analytical solutions.
Artificial Neural Networks for Wedge Diffraction Coefficients / G. Manara;P. Nepa;G. Pelosi;A. Scicchitano;S. Selleri. - STAMPA. - 3B:(2005), pp. 167-170. (Intervento presentato al convegno 2005 IEEE Antennas and Propagation Society International Symposium tenutosi a Washington, DC, USA nel 3-8 July 2005) [10.1109/APS.2005.1552461].
Artificial Neural Networks for Wedge Diffraction Coefficients
PELOSI, GIUSEPPE;SELLERI, STEFANO
2005
Abstract
The computation of the field diffracted from an impedance wedge is of relevant importance in the solution of high-frequency radiation and scattering problems. Few analytically exact or approximate diffraction coefficients for impedance wedge scattering have been presented in the literature. They are relevant to specific electrical and geometrical wedge configurations, and some exact solutions are computationally intensive to compute. An artificial neural network (ANN) performing such a computation is presented, with the objective of improving the numerical efficiency of the field evaluation procedure and to obtain a single tool spanning all the different domains of the known analytical solutions.
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