A Novel EM Parametric Modeling Method of Microwave Filters Incorporating Multivalued Neural Networks and Transfer Functions
Bibliographic record
Abstract
Model order reduction (MOR)-based neuro-transfer function (neuro-TF) method has become a trendy modeling technique for parametric modeling of microwave components. This article proposes a novel electromagnetic (EM) parametric modeling method for microwave filters incorporating multivalued neural networks (MNNs) and transfer functions (short for MNN-TFs). The original poles/zeros directly extracted through MOR are mismatched in different sequences for different geometrical samples, which is called the mismatch issue. In the proposed modeling approach, we develop an MNN-based pole-/zero-sorting algorithm to solve this issue. The proposed sorting algorithm introduces MNN to guide the sorting of poles and zeros with respect to geometrical variations. A classification method is proposed to divide the poles/zeros into subgroups for more effective sorting using MNNs. After the classification process, the poles/zeros in all the subgroups are automatically sorted using separate MNNs. Then the pole-/zero-matching is performed between the original poles/zeros and the predicted poles/zeros. The proposed sorting algorithm can obtain more continuous and smoother poles/zeros without EM sensitivity information. After the proposed sorting process, the sorted poles and zeros are used for preliminary training of neural networks, which can provide good initialization weights for the overall model. Finally, we perform overall neural network training to establish the MNN-TF model. The proposed method can obtain a more accurate overall model than the existing MOR-based neuro-TF methods, especially in cases of large geometrical variations. The trained MNN-TF model can be used for advanced circuit design, greatly accelerating the speed of high-level system design. The effectiveness of the proposed method is verified by two microwave examples of parametric modeling.
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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 | 0.000 |
| 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 | 0.000 | 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".