Application of neural networks in space-mapping optimization of microwave filters
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Bibliographic record
Abstract
In this paper, a design methodology combining coupling matrix representation of filters, neural models and space-mapping techniques is presented for further enhancement of optimization efficency of microwave filters. Neural models are developed for both initial dimension generation and design parameter sensitivity analysis. Combining neural models of filter substructures with space-mapping optimization, the total number of EM simulations of the complete filter structure is significantly reduced. The improvement in efficiency over conventional method is demonstrated using simulation and measurement results of both end-coupled and side-coupled waveguide dual-mode pseudo-elliptic filters. The total CPU times for design and optimization are reduced by 50% to 70 %.© 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it