Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications
Bibliographic record
Abstract
The recursive least-squares (RLS) algorithm stands out as an appealing choice in adaptive filtering applications related to system identification problems. This algorithm is able to provide a fast convergence rate for various types of input signals, which represents its main asset. In the current paper, we focus on the regularized version of the RLS algorithm, which also owns improved robustness in noisy conditions. Since convergence and robustness are usually conflicting criteria, the data-reuse technique is used to achieve a proper compromise between these performance features. In this context, we develop a computationally efficient approach for the data-reuse process in conjunction with the regularized RLS algorithm, using an equivalent single step instead of multiple iterations (for data-reuse). In addition, different regularization techniques are involved, which lead to variable-regularized algorithms, with time-dependent regularization parameters. This allows a better control in different challenging conditions, including noisy environments and other external disturbances. The resulting data-reuse regularized RLS algorithms are tested in the framework of echo cancellation, where the obtained results support the theoretical findings and indicate the reliable performance of these algorithms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".