A high-order model looking beyond the first-order harmonic superposition assumption
Bibliographic record
Abstract
In this paper, a hybrid high-order behavioural model is proposed to mimic the response of strongly nonlinear unmatched RF transistors. In this model, a high-order Multi-Harmonic Volterra (MHV) behavioural model is used to predict the DC and fundamental frequency components of the output signal, while higher harmonic components are predicted by the Poly-Harmonic Distortion (PHD) model. The added coefficients of the MHV model augment the first-order expansion (harmonic superposition) of the PHD model to improve the model accuracy where it is needed. The hybrid MHV-PHD model improves the DC drain current prediction by 5dB and fundamental frequency output-power by 2dB in terms of Normalized Mean Squared Error (NMSE), while improving overall time-domain prediction by 1dB.
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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".