A weighted local steady‐state determination approach based on the globally optimal economic steady‐states
Bibliographic record
Abstract
Abstract Steady‐state incremental constraints of manipulated variables play a vital role in making given steady‐states satisfy physical limitations and avoiding drastic set‐point changes. Nevertheless, some research reveals that the steady‐state incremental constraints will make the given locally optimal economic steady‐states diverge from the globally optimal economic steady‐states. Therefore, a novel weighted local steady‐state determination approach based on the globally optimal economic steady‐states is presented in this paper. Firstly, the globally and locally optimal economic steady‐states are both evaluated through considering and not considering steady‐state incremental constraints. Then, the angle between them is evaluated and the closest local steady‐state from the globally optimal economic steady‐state is calculated. Subsequently, a new weighted local steady‐state is evaluated by combining the locally optimal economic steady‐state and the closest local steady‐state, in which the weighting coefficient is carefully tuned based on the above‐calculated angle. Finally, several simulations verify that the proposed method could effectively shorten the settling time of controlled systems and improve their economic performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".