Bibliographic record
Abstract
A simple but reliable model tuning method was developed in order to tune a flight model for a high-fidelity type-specific small aircraft simulator.A genetic algorithm (GA) was used as a parameter estimation method.GAs are robust parallel heuristic search methods that often use least squares curve fitting methods to solve complex problems.They belong to the class of evolutionary computing algorithms that mimic natural processes, in this case evolution, to predict behaviour and solve optimization problems.A population of possible solution sets is selected at random and the known math model is then used to determine the behaviour of each of these possible solutions.The behaviour of each is then compared to the desired behaviour of the model, i.e., the reference data set, and the error is calculated.Those with the highest error are culled from the population while those with the lowest error are deemed to be "parents".These parent solution sets are then paired together, to create "children" by finding a weighted average of the parents.To ensure the solution space is fully explored, "mutations" are also created by replacing a single part of select solution sets with a randomly-generated value.Two mutation mechanisms were used in the algorithm described in this paper to ensure that the solution space was explored fully while avoiding convergence on a local, rather than global, minimum.The second generation solution set is 50% comprised of parents, 25% comprised of children, and 25% comprised of mutations.The process repeats until the convergence criteria is met.This algorithm was successfully tested with multiple dynamic systems, including simulated flight test data created using X-Plane, a flight simulator software.The algorithm proved to be a capable and adaptive parameter estimation method applicable to a wide variety of dynamic models, including flight models.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".