Effects of control loop delay on the stability of a rate control algorithm: Research Articles
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Bibliographic record
Abstract
This paper presents exact stability analysis of a rate control algorithm described in Perform. Eval. 2001; 43(2–3):63–94; Int. J. Commun. Systems 2001; 14(6):593–618. The stability regions of the rate control process in the presence of control loop delay are analysed. The rate control process is represented by delay-difference equation and the criteria for asymptotic stability are derived in terms of the control parameters and control loop delay. The analysis shows that the approximate upper bound of the control gain derived in Aweya et al. is very close to the exact bound developed here. Using theoretical calculations performed in the discrete-time domain, we show that as the feedback time delay d increases, the intensity of control (i.e. the control gain α) must decrease in order for the system to remain stable. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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