An investigation of the effects of fuzzy resolution on control inference using explicit relationships
Bibliographic record
Abstract
The number of partitions or fuzzy states that exist in a fuzzy variable determines the resolution of that variable. This paper investigates the effect of fuzzy resolution on the control action that is derived from a fuzzy-control rulebase. First, it is shown that some explicit relationships could be derived for the inference from a fuzzy rulebase in terms of input variables and known geometry of the membership functions. These relationships are established for a generic, single-input-single-output (SISO) fuzzy control rulebase that employs uniform triangular membership functions. Specifically, a formula for control action u is derived in terms of incremental input variable and corresponding membership grades and fuzzy resolution. The result is then extended to include global variations of the input variable. It is argued that the resulting equations could be used to demonstrate the applicability of fuzzy control rules in a wide variety of control situations. Also some useful properties that arise from these equations are identified.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".