Parametric Modeling of Microwave Filters by Combining Equivalent Circuit-Fitting-Based Transfer Functions and Neural Networks
Bibliographic record
Abstract
This study proposes an equivalent circuit-fitting-based neural transfer function modeling method (ECFB neuro-TF) to enhance the robustness, interpretability, and capability of handling nonideal factors in parametric modeling of coupled resonator microwave bandpass filters. Unlike previous approaches that rely on vector fitting (VF), this method introduces physical constraints through equivalent circuit (EC) mapping. Compared with the previous systematic neuro-transfer function method based on a compact embedded format (SCEF neuro-TF), this work includes the following advances: First, constraining pole/residue extraction via predefined circuit topology, effectively overcoming VF’s inherent issues of order inconsistency and parameter discontinuity. Second, establishing a complex exponential phase shift function through theoretical and experimental validation, providing accurate quantification of port effects. Furthermore, demonstrating the pole uniqueness and parameter continuity under perturbation from filter synthesis theory. The developed neuro-TF model incorporates these circuit-based features while employing neural networks to correct residual nonideal effects. Experimental results demonstrate that our method achieves superior accuracy over broader parameter ranges compared to both VF-based and multilayer perceptron (MLP) approaches, offering an efficient and reliable solution for complex electromagnetic (EM) modeling scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".