A blind approach to identification of Hammerstein-Wiener systems corrupted by nonlinear-process noise
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Bibliographic record
Abstract
This paper proposes a new blind approach to identification of Hammerstein-Wiener models, where a linear dynamics is embedded between two static nonlinearities. The blind approach directly aims at estimating immeasurable inner input and output, with noise effects in consideration. By exploiting input's piece-wise constant property, the parameters of the inverse output nonlinearity and the denominator of the linear dynamics are consistently identified via an iterative instrumental-variablebased method from the output measurements only; next, a subspace direct equalization method estimates the immeasurable inner input. The blind approach does not require an explicit parametrization of the input nonlinearity; moreover, the input nonlinearities are more general than static nonlinearities and may include finite-memory nonlinearities such as hysteresisrelay and hysteresis backlash. The proposed blind approach is validated and compared with the blind approach proposed by Bai in 2002 through numerical 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 | 0.000 | 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