Fuzzy Logic Method Use in F/A-18 Aircraft Model Identification
Bibliographic record
Abstract
A mathematical model for controlling the structural deflections of an F/A-18 modified aircraft was determined in the Active Aeroelastic Wing technology program. Five sets of signals from flight flutter tests corresponding to the excited inputs (differential ailerons, collective ailerons, collective stabilizers, differential stabilizers, and rudders) were measured at the NASA Dryden Flight Research Center. Two types of signals were used to build this new model: control deflections (the inputs) and structural deflections (the outputs). The fuzzy logic method was used in identifying the nonlinear aircraft models for 16 flight-test cases, based on Mach numbers (between 0.85 and 1.30) and altitudes (between 5000 and 25,000 ft). To find the best model, we tested a variety of systems with different numbers of inputs or fuzzy logic methods. By comparing the results obtained, we conclude that the best results, in terms of our preestablished specifications, were obtained for the 12-input Sugeno system.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".