Identification of a nonlinear model between control and structural deflections of an F/A-18 aircraft
Bibliographic record
Abstract
The aim of this paper is to determine the mathematical model between control deflections and structural deflections of the F/A-18 modified aircraft in the Active Aeroelastic Wing AAW program. Five excited sources were provided by NASA DFRC (Dryden Flight Research Center) from Flight Flutter Test (FFT). These excitations are: differential and collective ailerons, collective and differential stabilizers and rudders. We choose to use the Neural Network (NN) and fuzzy logic algorithms in order to identify the MIMO (Multi Input Multi Output) system for the F/A-18 aircraft. One of main contributions in this paper consists in the conversion of fuzzy logic algorithm results into neural network data. Then, these methods (NN and Fuzzy logic) were applied for the model identification and validation for sixteen flight conditions where Mach number varied from 0.85 to 1.30 and altitudes from 5,000 ft to 25,000 ft. Accurate results, expressed in terms of fit coefficients between estimated and measured signals higher than 99% were obtained, that led to the conclusion that the new methods here developed are very efficient.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".