Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Abstract

In this article, an advanced technique is developed to combine multi-output least-squares support vector regression (MLS-SVR) and pole-residue-based transfer function models for microwave filter parametric modeling. MLS-SVR is trained to learn the relationship between the length of tuning screws and the pole/residues of the transfer function, where MLS-SVR is an effective method to cope with the multi-output case unlike the traditional approach. Traditional approach treats the different outputs separately in the multi-output case and it cannot model the relation between different outputs. Another important element for modeling is feature parameters. Extracted feature parameters have an important influence on the accuracy of modeling. For the purpose of establishing a more accurate model, the complex system poles and residues from Y-parameters are chosen as the outputs of modeling, which are obtained by vector fitting (VF). Then we give a solution to obtaining pole/residues extracted by VF when the filter is in high detuned. After the proposed modeling process, trained model can be used to provide an accurate and fast prediction of the behavior of microwave filter with the length of tuning screws as variables, and model the electromagnetic simulation (or actual) microwave filter tuning. The methodology is applied to a narrow band coaxial-resonator filter modeling, and more accurate results are achieved compared with the other methods. An example is presented to illustrate the efficiency of the proposed method.