Event‐triggered output quantization control for asynchronously switched interval type‐2 fuzzy systems
Bibliographic record
Abstract
Abstract This article considers exponential stabilization (ES) of switched interval type‐2 (SIT2) fuzzy systems with asynchronous switching control. The SIT2 fuzzy system switches from one mode to another according to transition probabilities (TPs). A general SIT2 fuzzy quantized event‐triggered output controller (QETOC) is designed to save the communication resources. The QETOC employs distinctive membership functions from those of the IT2 fuzzy model and switches with mode‐pendent switching delay, which is more practical than asynchronously switched control techniques with a common switching delay and control schemes without TP. By designing discretized multiple Lyapunov function (DMLF) and using convex combination technique, sufficient conditions formulated by linear matrix inequalities (LMIs) are obtained to ensure that the DMLF does not ‘jump high’ at the moment of switching, and hence the conservatism of obtained results is significantly reduced since no additional dwell time is needed for the stabilization. It is discovered that the SIT2 fuzzy system is not necessary to be unstable on mismatched intervals, and it can be unstable on matched intervals. The advantages of theoretical analysis are verified by numerical simulations, and the classical assumption that each subsystem is unstable on mismatched intervals is conservative for control design.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".