Set‐based adaptive estimation for a class of uncertain nonlinear systems with output dependent nonlinearities
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Bibliographic record
Abstract
In this article, we consider the problem of parameter identification and state estimation of an uncertain continuous‐time linearly parameterized nonlinear system with output dependent nonlinearities subject to exogenous disturbances. A set‐based adaptive estimation is proposed in which the parameters and the states of the system are estimated along with an uncertainty set guaranteed to contain the true unknown values. The set‐update approach is such that the sets are updated only when an improvement in the precision of the parameter estimates and the state estimates can be guaranteed. The formulation provides robustness to parameter estimation error and bounded disturbances. The adaptive estimation technique can be viewed as an adaptive interval observer. Simulation examples are used to illustrate the effectiveness of the developed procedure and ascertain the theoretical results.
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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