Neural Networks and Support Vector Regression for the CRJ-700 Longitudinal Dynamics Modeling
Bibliographic record
Abstract
This paper presents a new methodology to identify the aerodynamic coefficients and predict the short-period and phugoid dynamics of an aircraft using two types of supervised learning models: artificial neural network (ANN), and support vector regression (SVR). The study was validated on the CRJ 700 regional jet. Simulated flight tests data were collected during various maneuvers performed on a Level D CRJ-700 Virtual Research Simulator (VRESIM) designed by CAE and Bombardier. Level D is the highest qualification for flight dynamics and propulsion models given by the FAA. Both ANN and SVR models were trained using the data collected from the VRESIM to develop multidimensional models capable of predicting the aerodynamic coefficients of the aircraft for any conditions in the flight envelope, defined by altitude, speed, weight, and center-of-gravity position. The choice of solvers and the optimization of hyperparameters are detailed for both types of models. These models were validated by comparing predicted flight parameters with experimental data obtained from the CRJ 700 Level D VRESIM considering the same pilot inputs. The results showed that both types of models (ANN and SVR) were able to reproduce with excellent accuracy the nonlinear behavior of the short-period and phugoid dynamics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".