An Arbitrarily Predefined-Time Convergent RNN for Dynamic LMVE With Its Applications in UR3 Robotic Arm Control and Multiagent Systems
Bibliographic record
Abstract
Zeroing neural network (ZNN), as a special type of recurrent neural network (RNN), is very competitive in solving time-varying linear matrix-vector equations. Recently, various ZNNs with predefined-time convergence (PTC) capabilities have been reported. Such ZNNs with PTC capabilities can achieve the predefined convergence time via explicitly presetting multiple parameters related to the upper bounds of their convergence time. However, obtaining suitable and robust values for these parameters through reasonable adjustments is a challenging task in many engineering applications. To address this problem, we propose a novel arbitrarily predefined-time convergent RNN (APTC-RNN) with a novel nonlinear piecewise activation-function (NPAF). Unlike most existing ZNNs with PTC capabilities, the proposed APTC-RNN, due to its NPAF, can achieve arbitrarily PTC (APTC) without adjusting any upper bound parameters. Furthermore, due to the piecewise computation form of the NPAF, the proposed APTC-RNN can provide a lower computational cost compared to most existing RNNs. The stability and APTC capability of the proposed APTC-RNN are proven by rigorous theoretical analysis and mathematical derivation. Numerical simulations show that APTC-RNN has faster and more accurate PTC capability than three state-of-the-art RNNs, while having less computational time. Finally, the practicality of the APTC-RNN is verified by applying it to the UR3 robotic arm and multiagent systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".