Improved RBF Neural Network Based Soft Sensor: Application to the Optimal Robust Calibration of a Six Degrees of Freedom Parallel Kinematics Manipulator
Bibliographic record
Abstract
Accuracy is paramount for the further development of parallel mechanism in real world, especially in industry. Previous research was focused on the improvement of rigidity and load capacity which is related with the stiffness matrix of closed loop kinematic structure. However, if the mechanical structure has been predefined or fabricated, stiffness matrix only can search for the optimal configuration in the workspace, but fails to enhance the accuracy at a given pose. In this research, the concept of optimal robust calibration is developed as an effective approach to largely reduce various errors of the predefined parallel mechanism. A novel coevolutionary radial basis function (RBF) neural network based soft sensor is proposed to implement the optimal robust calibration procedure. A six- degrees-of-freedom parallel kinematics manipulator is selected as the case study to verify the proposed methodology. The results demonstrate that the coevolutionary neural network possesses the excellent performance as a smart soft sensor for the calibration of closed loop kinematic structure.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".