Comparative Analysis of Machine Learning Regression Models for Unknown Dynamics
Bibliographic record
Abstract
System identification methods can enable scientists and engineers to model a system for analysis, estimation, or activity planning. Machine Learning (ML) regression algorithms are a useful data-driven system identification tool that can be used in many fields, such as signal identification, wireless communication, or dynamic modelling. However, researchers must select an appropriate algorithm depending on the system’s complexity. In this study, we evaluate the performance of three ML algorithms: Polynomial Fit, Artificial Neural Network (ANN), and Sparse Identification of Non-linear Dynamics (SINDy), to perform model identification in four different time-invariant dynamic environments. We trained each algorithm using 100 simulated data sets and validated them with ten different trajectories. We compare the results using an error distribution framework, demonstrating that ANN had the lowest prediction error, SINDy had comparable performance for three dynamic environments, but none of the algorithms reliably predicted the discontinuous accelerations. This study demonstrated that spacecraft control systems with continuous dynamics may benefit from ML methods.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".