Identification of closed-loop non-linear systems with structure optimization
Why this work is in the frame
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Bibliographic record
Abstract
Experiments to obtain input/output data for system identification of some systems can be conducted only with the controllers in action. Estimating an open-loop system model using closed-loop data offers many difficulties, particularly when noise is present in the measurements. In this paper, a new indirect non-linear system identification technique using a non-linear auto regressive moving average with exogenous input (NARMAX) structure and employing input/output data obtained from closed-loop experiments is described for the first time. Furthermore, a new method to estimate the linear multivariable open-loop transfer function from closed-loop data using an algebraic equation set is presented. The effectiveness of the proposed linear and non-linear identification approaches is illustrated by simulation studies on a non-linear physical system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it