A Method to Construct Equivalent Circuit Model From Frequency Responses With Guaranteed Passivity
Bibliographic record
Abstract
Converting the frequency response of a network into an equivalent time-domain circuit is a common task in several fields, such as power system simulations. A few good methods, such as the vector fitting method, have been proposed to do the conversion. Unfortunately, these methods sometimes produce non-passive equivalent circuits which are hard to realize in time domain or can lead to unstable simulations. In order to address the concern, this paper proposes a new conversion method for single-input single-output systems with guaranteed passivity for the resulting circuit. The basic idea of the proposed method is to represent the equivalent circuit as a matrix with varying dimension and unknown values. Genetic algorithm is then applied to find the values and dimension by minimizing the errors between the desired frequency response and that produced by the equivalent circuit. Since the equivalent circuit consists of only passive elements, the circuit passivity is always guaranteed. Details of the problem formulation and the solution algorithms are presented in this paper. Performance of the proposed method has been confirmed by several case studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.002 |
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".