Bibliographic record
Abstract
An open issue in Adaptive IIR Filtering (AIF) is that of convergence to a global minimum in the presence of observation noise, when the system is insufficiently modeled, or when the excitation source is colored. It is well known that algorithms based on Equation Error (EE) contain a single minimum that may be biased whereas, algorithms based on Output Error (OE) ensure the existence of an unbiased global minimum in presence of local minima. Recently, there have been a number of attempts to combine these formulations in order to ensure the existence and uniqueness of an unbiased minimum. The work presented here, Equation Error Output Error (EEOE) and Modified EEOE (MEEOE,) are such attempts in the context of system identification. Although the formulation of EEOE did not achieve the desired outcome and was later found out to be similar to that proposed by Kenny and Rohrs (1993), the exploration of its limitations, however, led to a superior algorithm namely, MEEOE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".