A robust set-membership normalized least mean-square adaptive filter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The performance of set-membership normalized least mean-square (SM-NLMS) adaptive filters deteriorates significantly in the presence of impulsive noise or interference. To solve this problem a new robust SM-NLMS (RSM-NLMS) algorithm is proposed. In addition, a framework to achieve robust performance in other algorithms of the set-membership (SM) family is developed. The proposed RSM-NLMS algorithm is compared with the conventional SM-NLMS and the robust normalized least mean-square (RNLMS) algorithms in impulsive-noise environments. Simulation results show that (1) the proposed RSM-NLMS algorithm has similar robustness with respect to impulsive noise as the RNLMS algorithm, (2) the RSM-NLMS and the conventional SM-NLMS algorithms offer reduced steady-state misalignment for the same convergence speed as compared to the RNLMS algorithm, and (3) the amount of computation is significantly reduced in the RSM-NLMS algorithm as it takes fewer weight updates to converge than the SM-NLMS and the RNLMS algorithms.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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