Parallel Trajectory Training of Recurrent Neural Network Controllers With Levenberg–Marquardt and Forward Accumulation Through Time in Closed-Loop Control Systems
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Bibliographic record
Abstract
This paper introduces a novel parallel trajectory mechanism that combines Levenberg-Marquardt and Forward Accumulation Through Time algorithms to train a recurrent neural network controller in a closed-loop control system by distributing the calculation of trajectories across Central Processing Unit (CPU) cores/workers depending on the computing platforms, computing program languages, and software packages available. Without loss of generality, the recurrent neural network controller of a grid-connected converter for solar integration to a power system was selected as the benchmark test closed-loop control system. Two software packages were developed in Matlab and C++ to verify and demonstrate the efficiency of the proposed parallel training method. The training of the deep neural network controller was migrated from a single workstation to both cloud computing platforms and High-Performance Computing clusters. The training results show excellent speed-up performance, which significantly reduces the training time for a large number of trajectories with high sampling frequency, and further demonstrates the effectiveness and scalability of the proposed parallel mechanism.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it