A cross-relation based affine projection algorithm for blind SIMO system identification
Bibliographic record
Abstract
A new time-domain adaptive algorithm is proposed for blind identi-fication of single-input multiple-output systems that is based on the cross-relation (CR) method. The proposed algorithm novelly ex-ploits the affine projection principle to minimize the CR error. As a result, a cost function is minimized that differs from the one used in existing CR based adaptive algorithms. A major advantage of the proposed multichannel affine projection algorithm (MCAPA) is that the affine projection order can be used to control the tradeoff between the rate of convergence and computational complexity. In an experimental study, MCAPA is compared with two recently de-veloped adaptive algorithms, i.e., the low-cost multichannel least-mean-square (MCLMS) and high-performance multichannel New-ton (MCN) algorithms. The results show that MCAPA converges faster than MCLMS with a computational complexity that is signif-icantly lower than MCN, thereby increasing the applicability of the CR method. 1.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".