Fuzzy Logic Method Use in F/A-18 Aircraft Model Identification
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it