Parameter identification and intersample output estimation of a class of dual-rate systems
Why this work is in the frame
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Bibliographic record
Abstract
For a class of dual-rate systems in which the input updating rate is an integer multiple of the output sampling rate, a frequency-domain model with a finite number of parameters is derived; based on this model, new algorithms for parameter identification and intersample output estimation using directly the dual-rate input-output data are proposed. We study convergence properties of the algorithms in detail in the stochastic framework and show: 1) the parameter estimation error consistently converges to zero under the persistent excitation condition; 2) the intersample output estimation error is uniformly bounded; and 3) the estimated output at the sampling instants based on the past input-output data has the property of minimum variance. Finally, we illustrate and test the proposed algorithms with an example system.
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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