A Generalized Asymmetric Prandtl-Ishlinskii Model for Characterizing Hysteresis Nonlinearities
Bibliographic record
Abstract
Smart material actuators, as an example, invariably exhibit hysteresis that may be either symmetric or asymmetric depending upon the actuation principle. A classical Prandtl-Ishlinskii model is normally used to describe the symmetric hysteresis. A generalized play operator is formulated and integrated to the Prandtl - Ishlinskii model together with density function to describe asymmetric hysteresis nonlinearities. This hysteresis operator is analysed using different envelop functions in order to illustrate the influence of these functions on the outputs of the generalized Prandtl-Ishlinskii model. Parameters identification for the envelope functions of the generalized play operator and the proposed density function are carried out using a nonlinear optimization technique. The validity of the generalized model is demonstrated by comparing its displacement responses with the measured asymmetric responses obtained for magentostrictive actuator. The results suggest that unlike the classical Prandtl-Ishlinskii model, the generalized Prandtl-Ishlinskii can effectively characterize asymmetric hysteresis properties.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".