Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this article is to determine the mathematical model between control deflections and structural deflections in an F/A-18 modified aircraft in the active aeroelastic wing programme. One future application would be the design of a flutter suppression model based on flight flutter tests. Five excited sources were provided by NASA Dryden Flight Research Center from flight flutter tests. These excitations, given by aircraft control surfaces, are: differential and collective ailerons, collective and differential stabilizers, and rudders. The neural network and fuzzy logic algorithms were chosen in order to identify the multi-input multi-output system for the F/A-18 aircraft. One main contribution of this article is the mapping of fuzzy logic algorithm results into neural network data. Then, these methods were applied for the F/A-18 model identification and validation for sixteen flight conditions expressed in terms of Mach numbers variations between 0.85 and 1.30 and altitudes varying between 5000 and 25 000 ft. Accurate results were obtained, expressed in terms of fit coefficients between estimated and measured signals greater than 99 per cent, which allows one to conclude that these new methodologies are very efficient for an aircraft identification and validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it