Parameter estimation of periodically switched linear systems
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Bibliographic record
Abstract
This study is concerned with parameter estimation of a special class of switched linear systems (SLSs), namely, periodically switched linear systems (PSLSs). General identification methods that do not explore switching sequence patterns may perform poorly in estimation accuracy and implementation efficiency. In this work, we first analyse input and output (I/O) data sequences and then establish the connection between the periodicity and I/O data sequence. This allows us to obtain an accurate estimation of the switching sequence period p0. We prove that the correctness of data classification is almost surely guaranteed. In implementation, we propose two efficient strategies, namely, the reverse order search and finite data selection, to improve the computational efficiency. Moreover, we provide both offline and online methods to estimate p0 and the parameters. The effectiveness of these methods are demonstrated in simulations.
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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 | 0.001 | 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 | 0.000 |
| 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