Computer diagnosis and tuning of RF and microwave filters using model-based parameter estimation
Bibliographic record
Abstract
This paper describes an efficient and robust approach for the computer diagnosis and tuning of RF and microwave filters relying upon model-based parameter estimation (MBPE) and multilevel optimization. The frequency sampled S-parameters are obtained from the measurement, and then an MBPE procedure based on adaptive sampling is employed to approximate the frequency-domain behavior of S-parameters in terms of rational functions. This approach uses a reduced-order system model. The effect of measurement noise is also considered. The approach is applied to coupled resonator filters that are modeled by a general equivalent circuit. The loss of each resonator is included in the model by a series resistor. A simple and efficient error function is used to reduce the computational effort of the optimization while improving the speed and robustness of diagnosis process for lossy filters. This approach can be applied to many classes of filters. The proposed approach is demonstrated through numerical examples and application to the manufactured filter.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".