Title :
Artificial neural networks for wedge diffraction coefficients
Author :
Manara, G. ; Nepa, P. ; Pelosi, G. ; Scicchitano, A. ; Selleri, S.
Author_Institution :
Dept. of Inf. Eng., Pisa Univ., Italy
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.
Keywords :
computational electromagnetics; electric impedance; electromagnetic wave scattering; geometrical theory of diffraction; neural nets; GTD; UTD; artificial neural networks; geometrical theory of diffraction; high-frequency radiation problems; impedance wedge; scattering problems; wedge diffraction coefficients; Anisotropic magnetoresistance; Artificial neural networks; Computer networks; Electromagnetic diffraction; Electromagnetic scattering; Electromagnetic wave polarization; Performance analysis; Performance evaluation; Surface impedance; Telecommunication computing;
Conference_Titel :
Antennas and Propagation Society International Symposium, 2005 IEEE
Print_ISBN :
0-7803-8883-6
DOI :
10.1109/APS.2005.1552461