Use of eigenvector expansions in generalized Wiener system models
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Bibliographic record
Abstract
The authors consider the estimation of Wiener and Volterra kernels of finite dimension, finite order, finite memory nonlinear systems. The linear filters in a generalized Wiener model are constructed using the parallel cascade identification method. The authors demonstrate that these filters span the subspace occupied by the linear part of the system being identified. This approach is compared with the Laguerre expansion method, in which a complete orthonormal basis of the linear space is created. Simulations are used to illustrate the strengths and weaknesses of each approach.
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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 |
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