Enhanced cognition‐driven formulation of space mapping for equal‐ripple optimisation of microwave filters
Bibliographic record
Abstract
A recently introduced cognition‐driven formulation of space mapping (SM) is an efficient method for equal‐ripple optimisation of microwave filters. Feature frequency parameters and ripple height parameters are utilised. The technique requires the assumption that the initial number of feature parameters is correct, and uses an equally divided passband specification as a preliminary target. The present study proposes an enhanced technique which can correct the number of feature frequency parameters, thus it can work well even if the filter response of the initial point has an incorrect number of feature frequency parameters. Additionally, the enhanced technique incorporates filter design knowledge of the Chebyshev filter function into cognition‐driven SM. The authors propose to use the feature frequency parameters of the Chebyshev filter function response curve to obtain the target for cognition‐driven SM. A new trust region mechanism handles new parameters in the proposed process of correcting the number of feature frequency parameters and guarantees convergence. The technique is suitable for the design of filters with equal‐ripple responses. It is illustrated by two microwave filter examples.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".