• DocumentCode
    3385595
  • Title

    Parametric modeling of millimeter-wave passive components using combined neural networks and transfer functions

  • Author

    Gongal-Reddy, Venu-Madhav-Reddy ; Feng Feng ; Qi-Jun Zhang

  • Author_Institution
    Dept. of Electron., Carleton Univ., Ottawa, ON, Canada
  • fYear
    2015
  • fDate
    25-27 May 2015
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    This paper propose to develop the combined neural networks and transfer functions (neuro-TF) for parametric modeling of millimeter-wave passive components. Artificial neural networks (ANN) techniques are recognized as a powerful tool for modeling the EM behavior of microwave components. In this paper, we train the ANN to map geometrical variables onto coefficients of transfer functions. The model obtained using our proposed technique can achieve good accuracy, and can be further used in the high-level design. Two millimeter-wave examples are used to demonstrate the validity of this technique.
  • Keywords
    coplanar waveguide components; neural nets; substrate integrated waveguides; transfer functions; ANN; EM behavior; artificial neural networks; microwave components; millimeter-wave passive components; neuro-TF; parametric modeling; transfer functions; Coplanar waveguides; Data models; Millimeter wave technology; Neural networks; Substrates; Training; Transfer functions; Modeling; coplanar waveguides; millimeter-waves; neuro-TF model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Millimeter Waves (GSMM), 2015 Global Symposium On
  • Conference_Location
    Montreal, QC
  • Type

    conf

  • DOI
    10.1109/GSMM.2015.7175449
  • Filename
    7175449