Parametric Modeling Incorporating Joint Polynomial-Transfer Function With Neural Networks for Microwave Filters
Bibliographic record
Abstract
This article proposes a novel parametric modeling technique incorporating a joint polynomial-transfer function with neural networks (short for neuro-PTF) for electromagnetic (EM) behaviors of microwave filters. In the proposed technique, the polynomial function is introduced together with the pole-residue-based transfer function to represent the EM responses. The pole-residue-based transfer function is used to represent the whole EM response at the beginning and is subsequently divided into multiple subtransfer functions where each subtransfer function contains one pair of pole/residue. A novel smoothness-discriminating algorithm is proposed to judge the smoothness of each subtransfer function response and separate the pole/residue pairs whose subtransfer function response is determined to be smooth. The proposed method introduces low nonlinear polynomial functions to re-fit the smooth parts of subresponse and remains the nonsmooth parts of subresponse for the highly nonlinear transfer functions to represent. By this way, the proposed method avoids the discontinuity problems of nonunique parameter extraction caused by fitting smooth curves using the highly nonlinear transfer function. Neural networks are proposed to learn the relationship between the polynomial coefficients/poles/residues and the geometrical parameters. Utilizing both advantages of the polynomial functions and transfer functions, the proposed method can produce more accurate models than the existing neuro-TF methods, especially with large geometrical variations. The accuracy and robustness of the proposed technique are demonstrated using three EM application examples of microwave filters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".