Bibliographic record
Abstract
This paper deals with the control of the class of uncertain hybrid stochastic systems. The uncertainties we are considering are of norm bounded type. The stochastic stabilization and robust stabilization problems are treated. LMIs based conditions are developed to design the state feedback controller with constant gain that stochastically (robust stochastically) stabilizes the studied class of systems. Numerical examples are given to show the usefulness of the proposed results. Resume Cet article traite de la commande des systemes incertains stochastiques a sauts markoviens. Les incertitudes considerees dans ce travail sont du type bornees en norme. Les problemes de stabilite et de stabilisation de cette classe de systemes sont consideres ainsi que leur robustesse. Des conditions en forme d’inegalites matricielles lineaires pour le design d’un correcteur par retour d’etat a gain constant (indepdent du mode du systeme) sont developpees. Des exemples numeriques pour montrer l’efficacite des resultats developpes sont presentes. Les Cahiers du GERAD G–2004–28 1
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".