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.

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

“…As proofed in section II (c), the surrogate model based on feature-based objective function will smooth the design landscape which avoid the optimization trapping into local optima. Not only in EM optimization, the feature-assisted surrogate can also be utilized in filter tuning [82], yield estimation [72]- [73], diplexer optimization [84], etc.…”

A parametric model for cavity filter using multiple outputs least‐squares fuzzy support vector regression

“…In Reference [7], the multiple output least-square multi-support vector regression method was proposed to solve the multidimensional output problem of the support vector regression algorithm, and the correlation of input variables was considered. The deficiency was the lack of data analysis and dimensionality reduction before modeling, which increases the amount of calculation and decrease the generalization ability of the model.…”

Tuning model for microwave filter by using improved back‐propagation neural network based on gauss kernel clustering

“…The limitation was that it assumed a linear relationship between the tuning elements and the M , and it applies only to the filter of simple topology. A data‐driven the least squares multi‐core support vector machine was proposed to build the tuning model, however, the data used in tuning are derived from electromagnetic simulation software, which can hardly reflect the tuning rules of actual filters. Due to the complex structure of the actual filter, modeling and tuning by HFSS becomes an extremely tedious task.…”