A Novel Neuro-TF Modeling Technique Incorporating Parametric Sanathanan–Koerner Iteration of Continuous Pole/Residue Extraction
Bibliographic record
Abstract
This article proposes a novel neurotransfer function (neuro-TF) modeling technique incorporating parametric Sanathanan–Koerner iteration (S–K) of continuous pole/residue extraction for electromagnetic (EM) parametric modeling of microwave components. For the first time, parametric S–K iteration is applied to extract the coefficients of the transfer function instead of vector fitting for neuro-TF modeling. In the process, S–K iteration is introduced to construct and derive the novel and systematic formulation for continuous pole/residue extractions with respect to geometrical parameter variations. Since the coefficients follow the geometrical basic function within our limits, they can remain smooth when being extracted. Due to the robustness issue for the neuro-TF modeling, those extracted coefficients need to be converted to poles and residues. The continuity of poles/residues in the conversion process is rigorously proven. Compared with the existing quadratic approximation vector fitting method of poles/residues extraction and standard vector fitting method, the parametric S–K iteration can provide better coefficient continuity and smoothness with strong robustness. The developed models using the proposed method can achieve better modeling accuracy in a larger range of geometrical parameters. Three microwave filters are employed as examples to verify the effectiveness of the proposed technique.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".