Online modeling and prediction of a hydraulic force-acting system using neural networks
Bibliographic record
Abstract
Investigates the experimental modeling of the dynamic behavior of a force-acting industrial hydraulic actuator using a neural network (NN). Due to variable environmental stiffness as well as the characteristics of hydraulic components, the dynamics of the system is time-varying and highly nonlinear. It is therefore desirable to develop a nonlinear modeling scheme, based on NNs, to estimate and predict the output of the system online. In this paper, the predictability of an online-trained NN modeling a hydraulic force-acting system is first compared to a linear model. The result demonstrates that the NN outperforms its linear counterpart in terms of multi-step prediction. Then, a more detailed discussion of the online training of the NN is provided. The related aspects include the choice of the window length, the NN's structure and the criterion for terminating the training. The work studied in this paper should help in the design of appropriate force-control law and/or fault diagnosis algorithms.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".