Iteratively reweighted correlation analysis method for robust parameter identification of multiple‐input multiple‐output discrete‐time systems
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Bibliographic record
Abstract
In the engineering practices, the distributions of measurements are non‐Gaussian as they contain outliers. As some slight deviations from the Gaussian assumption would probably cause the performance of an estimator to degrade significantly, a novel iteratively reweighted correlation analysis method is proposed for robust parameter estimation of multiple‐input multiple‐output (MIMO) systems, in the presence of Student's t‐ noises. The iterative method achieves good robustness and high efficiency by the combination of multivariable correlation analysis and t‐ distribution based M‐ estimators. The appropriate updating weights are able to enter into the sample cross‐correlation function, so that the heavy tails are lowered, and the impact of outliers is weakened to the greatest extent. Based on the robust finite impulse response models, the identification procedure is then to reconstruct the noise‐free estimates to identify the parameters of an MIMO system. The theoretical discussions and simulation results demonstrate that the proposed method works well.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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