Problem of stochastic control of enterprise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction.In this work we extend the approach of the previous researches to the measurement feedback case.We remove the assumption that the state of the system is available for feedback and show how algorithms from the previous researches can be used in the measurement feedback case.We derived solvability conditions for the problem but analytical computation of the optimal controller turned out to be extremely difficult task.The feasibile approach is to use model predictive control technique.So far, we have obtained several computational algorithms for model predictive control of constrained systems that are subject to stochastic disturbances.These results have been based on the assumption that all states of the plant are available for feedback.Resultst.In this scientific work, we consider the more general case in which we assume that output of the plant is measured and available for feedback.In this case, static feedbacks are no longer sufficient and we need to study dynamic feedbacks.We consider the plant given by the discrete time state space equations
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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