A direct approach to identify closed loop Wiener systems, whose linear dynamics are open-loop unstable
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Bibliographic record
Abstract
A Wiener system is a series connection of a linear dynamic system followed by a static non-linearity. The identification of Wiener systems has been an active research topic for years. We extend the algorithm proposed by Zhao & Westwick, [Proceedings, ACC2003] (2003) to identify Wiener systems that are unstable in open loop, but being operated stably in a closed-loop configuration. The variant of the MOESP (multivariable output-error state space) algorithm developed in Y. Zhao & D.T. Westwick (2003) is used to identify a state space model of the linear part of a Wiener system operating in closed loop. Since the linear dynamics of the Wiener system are unstable in open loop, the output of the linear subsystem cannot be obtained by direct simulation. Without an estimate of the linear output, the nonlinearity can't be estimated. The main contribution of this paper is the design of an extended Kalman filter, which is used to estimate the states of the linear subsystem as well as the parameters of the nonlinearity.
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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 | 0.001 | 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