On Nonparametric Identification of Multi-Channel Hammerstein Systems
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Bibliographic record
Abstract
The paper deals with the problem of identification of a class of nonlinear dynamical systems of the multi-channel form. The examined system is the multi-channel generalization of the classical Hammerstein model. The a priori information about the system nonlinearities is very limited excluding any parametric approach to the problem. The modern statistical theory of nonparametric regression along with the marginal integration approach are applied to form estimates of the nonlinearities. In particular the generalized kernel regression techniques are used to construct the identification algorithms. Pointwise convergence rates of the proposed estimates are evaluated. A striking feature of one of our identification algorithm is its ability to decouple the estimation problem related to each channel. This is a surprising result since the input signals are dependent with completely unknown joint probability density function.
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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 | 0.001 | 0.001 |
| 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