A New Training Approach for Robust Recurrent Neural-Network Modeling of Nonlinear Circuits
Bibliographic record
Abstract
A new approach for developing recurrent neural-network models of nonlinear circuits is presented, overcoming the conventional limitations where training information depends on the shapes of circuit waveforms and/or circuit terminations. Using only a finite set of waveforms for model training, our technique enables the trained model to respond accurately to test waveforms of unknown shapes. To relate information of training waveforms with that of test waveforms, we exploit an internal space of a recurrent neural network, called the internal input-neuron space. We formulate a new circuit block combining a generic load and a generic excitation to terminate the circuit. By sweeping the coefficients of the proposed circuit block, we obtain a rich combination of training waveforms to cover the region of interest in the internal input-neuron space effectively. We also present a new method to reduce the amount of training data while maintaining the necessary modeling information. The proposed method is demonstrated through examples of recurrent neural-network modeling of high-speed drivers and an RF amplifier. It is confirmed that, for different terminations and test waveforms, the model trained with the proposed technique has better accuracy and robustness than that using the existing training methods.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".