A Bootstrap Term Selection Method for the Identification of Time-Varying Nonlinear Systems
Bibliographic record
Abstract
Most physical systems are nonlinear and often time-varying. Constructing accurate models for nonlinear systems require specialized model structures that include their nonlinearities, whereas models of time-varying systems must include the time courses of the model's parameters. This contribution implements a technique in which the time dependence of the system's parameters are modeled by projecting them onto one or more expansion bases. However, unless an appropriate set of bases is chosen, it is likely that many of the basis functions used in the expansion will not contribute appreciably to the final model and may cause inaccuracy in the parameter estimates. Our study addresses the selection of a minimal number of parameters for optimization from a basis expansion of a system's time variations. The bootstrap method is used to select significant basis coefficients and hence basis functions in order to improve the overall accuracy of the model. The performance of the algorithm is demonstrated on simulated data from a system used to model the reflex contribution to joint stiffness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".