Sequential neural network model for the identification of magnetorheological damper parameters
Bibliographic record
Abstract
Abstract Magnetorheological (MR) dampers exhibit a complex nonlinear hysteresis which makes the modeling of their behavior with parametric or non-parametric models to be challenging. In case of parametric models, the generalization of the parameters identified for a particular excitation is difficult and requires high computation costs. On the other hand, non-parametric models are considered as black-box type with no association to physical phenomena. The objective of this study is to propose a new identification model combining the merits of a parametric model and neural network paradigm. The proposed model is a parametric type which exploits an algebraic model with a hyperbolic tangent hysteresis, while a series multilayer-perceptron (MLP) neural networks are used to identify the model parameters under different excitation conditions. This approach not only preserves the physical meanings of the model parameters but also prompts generalization to common excitation conditions. The data for training the MLP neural networks were generated from a test program on a RD-8041-1 MR damper covering a wide range of input conditions. Results show that the proposed sequential neural network model not only increases the accuracy of the predicted MR damper force but also exhibits higher robustness and better consistency under different excitation conditions than a conventional algebraic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".