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
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;
Conference_Titel :
Millimeter Waves (GSMM), 2015 Global Symposium On
Conference_Location :
Montreal, QC
DOI :
10.1109/GSMM.2015.7175449