Time-domain response and sensitivity of periodically switched nonlinear circuits
Bibliographic record
Abstract
This paper presents a new sampled-data simulation method for analysis of the response and sensitivity of periodically switched circuits with mildly nonlinear elements at time points of an equal interval. Using the Volterra functional series and interpolating the Fourier series, the method extends the sampled-data simulation algorithm of linear circuits to periodically switched nonlinear circuits. In addition, it extends the two-step algorithm for periodically switched linear circuits to periodically switched nonlinear circuits to handle inconsistent initial conditions encountered at switching instants. The method is a general computer-oriented formulation method. It does not require costly Newton-Raphson iterations. Instead, it performs preprocessing computation of a set of constant matrices and vectors that are used in subsequent simulation steps. The response and sensitivity at time points of an equal space are obtained using elementary matrix algebra. The method is most effective if the nonlinear characteristics of circuits is mild and computationally efficient if the response and sensitivity over a long period of time are needed. The method has been implemented in a computer program. Simulation results on example circuits are compared with those from PSPICE, brute-force (BF), charge conservation, and linear multistep predictor-corrector methods to show the speed and accuracy of the method. The normalized difference between the response and sensitivity of example circuits obtained from the proposed method and those from PSPICE and BF at nonswitching time points is below 0.5%. The accuracy of the method at switching time points is verified using charge conservation.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".