Design and Application of an Improved Least Mean Square Algorithm for Adaptive Filtering
Why this work is in the frame
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Bibliographic record
Abstract
This paper enumerates the strengths and defects of the traditional least mean square (LMS) algorithm for adaptive filtering, and then designs a novel LMS algorithm with variable step size and verifies its performance through simulation. In our algorithm, the step size is no longer adjusted by the square of the error (e2(n)), but by the correlation between the current error and the error of a previous moment e(n-D). In this way, the algorithm becomes less sensitive to the noise with weak autocorrelation, and manages to achieve fast convergence, high time-varying tracking accuracy, and small steady-state error. The simulation results show that our algorithm outperformed the traditional LMS algorithm with fixed step size in convergence speed, tracking accuracy and noise suppression. The research findings provide a new tool for many other fields of adaptive filtering, such as adaptive system identification and adaptive signal separation.
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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 | 0.000 |
| 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