PSIJ Transfer Function Response Prediction via NARNET and KBNNs
Bibliographic record
Abstract
In this paper, nonlinear autoregressive neural network is combined with knowledge-based neural network in order to develop an efficient method that further expands the bandwidth of the jitter transfer function. In the proposed hybrid approach, a knowledge-based neural network is developed using the training data from two types of models: fast-to-evaluate analytical model for jitter transfer function and computationally expensive circuit simulator generated accurate jitter transfer function response. Knowledge-based neural network can efficiently produce relatively accurate prediction of the jitter profile within the desired frequency range, using which a large number of data points is generated. In the next step, nonlinear autoregressive neural network is trained using data obtained from knowledge-based neural network. Proposed nonlinear autoregressive neural network ensures reasonable accuracy even beyond the frequency range of the original accurate data that is used in developing the knowledge-based neural network. A case study with 32nm CMOS technology is presented to demonstrate the validity of the proposed approach compared to a circuit simulator.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".