A Novel Variable-Parameter Variable-Activation-Function Finite-Time Neural Network for Solving Joint-Angle Drift Issues of Redundant-Robot Manipulators
Bibliographic record
Abstract
This article presents a novel variable-parameter variable-activation-function finite-time neural network (VPA-FTNN) to deal with joint-angle drift issues of redundant-robotic arms. Different from most existing recurrent neural networks, VPA-FTNN establishes an error-based finite-time-convergence neural dynamics equation with variable-parameter and variable-activation-functions features so that it can effectively deal with the joint-angle drift of redundant-robotic arms with higher convergence speed and accuracy. It should be noted that VPA-FTNN can achieve finite-time convergence without relying on special activation functions. In order to verify the advantages of the proposed VPA-FTNN, it is compared with the varying-parameter convergent-differential neural network and traditional Zhang neural network when dealing with the joint-angle drift. Simulation and physical experiment results demonstrate the effectiveness and practicality of the proposed VPA-FTNN in solving the redundant robot joint-angle drift problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".