Generating rules in selecting controller gains: a combined rough sets/fuzzy sets approach
Bibliographic record
Abstract
The combined application of approximate time windows (ATW) and rough sets in deriving rules for tuning controllers is discussed. The approach is to build an approximate reasoning system based approximate measures of controller performance, namely, overshoot, rise time, and settling time. Overshoot is the biggest deviation of step response from a particular steady state after the step response has reached a tolerance band for the first time. Rise time r/sub t/ is the time when a step response reaches 90% of its steady-state value for the first time, and settling time s/sub t/ is measured relative to rise time (i.e. the clock for s/sub t/ is reset at t=r/sub t/). In this paper, the clocks used to measure durations required to achieve controller objectives are modeled as ATWs. An ATW partitions time relative to granules (clumps of similar timing measurements) such as early, ontime, late. An ATW determines the degree of membership of each observed duration in each of its temporal partitions. Based on observations of the degree of overshoot, rise time, and settling time during the operation of a control system, the architecture of an approximate time rough control system is established. The rough controller is guided by rules derived from a real-time decision-making system. The focus of this paper is a description of how rough control rules derived from a real-time decision system table have been used in fine-pointing for attitude control of a small satellite. The contribution of this paper is the application of rough sets, fuzzy sets and approximate time windows in the design of approximate time rough control systems.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".