Using the SSEMGP – SEMI - STATE Equation Matrix Generation Program for Transfer Function Generation in Analog Circuits with Single Input – Single Output and Multiple Inputs - Multiple Outputs
Bibliographic record
Abstract
The main objective of symbolic analysis is to derive the symbolic expressions of circuit functions and to compute their sensitivities, thereby enabling the evaluation of circuit characteristics and their variation with respect to parameter values. This paper presents an approach for generating transfer functions in both SISO (single-input-singleoutput) and MIMO (multiple-input-multiple-output) systems. The method is based on the circuit's semi-state equations. Using this semi-state equation technique, a computational program called SSEMGP (Semi-State Equation Matrix Generation Program) was developed. This program symbolically and numerically produces the matrices associated with the semistate equations ($W, G, B$, and$L^{\boldsymbol{t}}$) and supports a complete circuit analysis. It automatically formulates symbolic equations, generates various types of circuit functions in symbolic or partially symbolic form, and produces matrix representations for systems with multiple inputs and outputs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".