Identification and validation of a F/A-18 model Using Neural Networks
Bibliographic record
Abstract
In this paper, a new approach to identify and valid ate the F/A-18 aeroservoelastic model based on flight flutter tests is presented. The Neu ral Network, trained with five different flight flutter cases, is validated using eleven oth er flight flutter test data. The total of sixteen flight flutter tests cases were obtained fo r all three flight regimes (subsonic, transonic and supersonic) at Mach numbers between 0.85 and 1.30 and altitudes between 5,000 feet and 25,000 feet. The obtained r esults highlight the efficiency of the multi-layer perceptron Neural Network in model identification. The Neural Network optimization is required mixing hidden layer size r eduction and four-layered Neural Network performances. This article shows that a four-layered Neural Network with only 16 neurons is sufficient to create an accurate mode l. The fit coefficients are higher than 92%, either for the identification test data or the validation ones, and thus the Neural Network accuracy was demonstrated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".