Development of Reduced Preisach Model Using Discrete Empirical Interpolation Method
Bibliographic record
Abstract
The Preisach model, which is constructed by the superposition of relay operators, is one of the most popular hysteresis models to describe the hysteresis nonlinearities in smart-materials-based actuators. The application of the Preisach model suffers from the tradeoff between the model accuracy and the number of the relay operators. With a large number of relay operators, the Preisach model can predict the hysteretic effect very precisely; however, a large number of relay operators may lead to a heavy computation burden. To deal with this tradeoff, in this paper, a model order reduction method, namely discrete empirical interpolation method, is applied to reduce the number of the relay operators and meanwhile to preserve the model accuracy of the original Preisach model. Simulations under different conditions (different input signals and different density functions) and experimental tests on a magnetostrictive-actuated platform are conducted to validate the effectiveness of the proposed reduced Preisach model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".