A Practical Regularized Recursive Least-Squares Algorithm for Robust System Identification
Why this work is in the frame
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Bibliographic record
Abstract
The recursive least-squares (RLS) adaptive filtering algorithm is frequently used in system identification problems. The popularity of this algorithm is mainly related to its fast convergence rate. In this context, the main parameter that controls the convergence features of the RLS filter is the forgetting factor. On the other hand, in noisy environments, the robustness of the algorithm can be improved by using an appropriate regularization term. In this paper, we propose a regularized RLStype algorithm, by considering a linear state model and following the weighted least-squares optimization criterion. The resulting optimal regularization parameter also includes a specific term related to the model uncertainties, which is estimated in a practical manner within the algorithm. Simulation results obtained in the framework of echo cancellation support the performance features of the regularized RLS algorithm, which could represent an appealing solution for robust system identification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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