New Identification Method Based on Neural Network for Helicopters from Flight Test Data
Why this work is in the frame
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Bibliographic record
Abstract
A new technique for helicopter model identification from flight data tests is presented. A helicopter model has been identified and validated for 22 flight conditions defined by an altitude varying between 3,000 ft and 6,000 ft, a s peed varying from 30 knots to 110 knots and a helicopter loading with a heavy gross weight and a longitudinal aft or forward center of gravity, and for different pilot commands. The d ynamic behavior of the helicopter was identified with a recurrence method and an optimization procedure based on neural network theory and tuning of the initial conditions . Observation of the model’s output was carried out by three different methods (linear and nonlinear) and a comparison between them was performed. Very good results were obtained in both open- and closed-loop systems.
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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 | 0.000 | 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 | 0.001 | 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