Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Hybrid-Based Transfer Functions

Abstract

Neuro-transfer function (neuro-TF) approaches have become more and more popular in parametric modeling for electromagnetic (EM) behavior of microwave components. Existing pole-residue-based neuro-TF approach has better capability of dealing with high-order problem than the rational-based neuro-TF approach, but has the discontinuity issue and the associated non-smoothness issue of the poles/residues when the geometrical variations become large while the rational-based neuro-TF approach does not have. This paper addresses this situation and presents a novel hybrid-based neuro-TF technique which systematically combines both pole-residue and rational formats of the transfer functions. Starting with the pole-residue-based transfer functions, we propose a novel technique to automatically identify the poles/residues that are smooth-continuous and the poles/residues that have the discontinuity and non-smoothness issues. The proposed technique converts the poles/residues that have those issues into the coefficients of the rational-based transfer function to solve the discontinuity and non-smoothness issues in the existing pole-residue-based neuro-TF approach. The proposed technique remains the smooth-continuous poles/residues in the pole-residue format of the transfer function to maintain the capability of handling high-order problem. Compared with the existing neuro-TF modeling methods, the proposed technique can obtain better accuracy in challenging applications of large geometrical variations and high order. The proposed technique is illustrated by two examples of parametric modeling of microwave components.

Keywords

• Parametric modeling
• microwave components
• neural networks
• hybrid-based transfer function
• parameter extraction