Noisy Autoregressive System Identification Based on Repeated Autocorrelation Function
Bibliographic record
Abstract
This paper presents an identification approach for the minimum-phase autoregressive (AR) systems in the presence of heavy noise based on a repeated autocorrelation function (RACF) of observed data. It is shown that RACF retains poles of the original system and in noisy environment if it is used instead of single ACF in the modified least-squares Yule-Walker equations the effect of additive noise can be reduced. A termination criterion for the repeated operations is proposed based on the decaying nature of correlation values. The length of ACF, which is kept fixed in all RACFs, is determined from the decorrelation time of the single ACF. Simulation results show the superiority of performance by the proposed method in comparison to some of the existing methods in estimating the AR parameters even at a very low SNR of -5 dB
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".