Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive Control*
Why this work is in the frame
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Bibliographic record
Abstract
A novel recurrent neural network (RNN)-based approach is proposed in this work to handle joint chance-constrained stochastic model predictive control (SMPC) problem. In the proposed approach, the joint chance constraint (JCC) is first reformulated as a quantile-based inequality to reduce the complexity in approximation. Then, the quantile function (QF) in the quantile-based inequality is replaced by the empirical QF using sample average approximation (SAA). Afterwards, the empirical QF is approximated via an RNN-based surrogate model, which is embedded into the SMPC problem formulation to predict quantile values at different sampling instants. By employing the RNN-based approximation, the resulting deterministic optimization problem is finally solved through a nonlinear optimization solver. The proposed approach is applied to a hydrodesulphurisation process to demonstrate its efficiency in handling the SMPC problem.
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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 | 0.000 | 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 it