Compact MOR-Based Neuro-TF Parametric Modeling Incorporating In-Band Approximation and Out-of-Band Conversion Technique
Bibliographic record
Abstract
This article proposes a compact model order reduction (MOR)-based neuro-transfer function (neuro-TF) parametric modeling method incorporating in-band approximation and out-of-band conversion techniques for passive microwave components. This method uses complex exponent-pole–zero TF to represent the EM response of microwave devices. The TF based on pole-zero gain format from MOR is used to represent the EM response at first. Then, the poles/zeros are classified to divide the TF into the in-band pole-zero function and the out-of-band complex exponential function. The frequency linear approximation (FLA) is used to guide the classification of poles/zeros and the sorting of in-band poles/zeros. The conversion of complex exponential functions is used to solve the mismatch and nonlinear issue of out-of-band poles and zeros. This method avoids the complex mismatch issue of all poles and zeros. Neural networks are employed to capture the relationship between geometrical parameters and the coefficients of complex exponential functions, poles, and zeros. In comparison to traditional MOR-based neuro-TF parametric modeling techniques, the proposed approach demonstrates superior accuracy and robustness, particularly when dealing with substantial variations in geometrical parameters. The effectiveness of this method is confirmed through three examples involving microwave filters.
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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 | 0.000 | 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 | 0.001 |
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".