A fast and accurate piezoelectric actuator modeling method based on truncated least squares support vector regression
Bibliographic record
Abstract
In order to improve the applicability of piezoelectric actuators (PEAs) in precision positioning, least squares support vector regression (LS-SVR) is applied to model hysteresis in PEAs due to its high modeling accuracy and fast convergence speed. However, low robustness of LS-SVR makes modeling accuracy susceptible to noises, which makes LS-SVR hysteresis models difficult to be applied in engineering environment. In this article, a robust truncated least squares support vector regression (T-LSSVR) is proposed. With the truncation strategy, redundancy in the training set is reduced and robustness is improved. Parameters required for T-LSSVR are optimized by particle swarm optimization and cross optimization algorithms. To test the proposed approach, it is applied to predict the hysteresis of PEAs. Results show that the proposed method is more accurate and robust than other versions of LS-SVR when the training set is polluted by noises, and meanwhile reduces the sample size and increases computational efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".