• DocumentCode
    445172
  • 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
  • Volume
    3B
  • fYear
    2005
  • fDate
    3-8 July 2005
  • Firstpage
    167
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Antennas and Propagation Society International Symposium, 2005 IEEE
  • Print_ISBN
    0-7803-8883-6
  • Type

    conf

  • DOI
    10.1109/APS.2005.1552461
  • Filename
    1552461