A machine learning-based state estimation approach for varying noise distributions
Bibliographic record
Abstract
The field of estimation theory is concerned with providing a system with the ability to extract relevant information about the environment, resulting in more effective interaction with the system’s surroundings through more well-informed, robust control actions. However, environments often exhibit high degrees of nonlinearity and other unwanted effects, posing a significant problem to popular techniques like the Kalman filter (KF), which yields an optimal only under specific conditions. One of these conditions is that the system and measurement noises are Gaussian, zero-mean with known covariance, a condition often hard to satisfy in practical applications. This research aims to address this issue by proposing a machine learning-based estimation approach capable of dealing with a wider range of noise types without the need for a known covariance. Harnessing the generative capabilities of machine learning techniques, we will demonstrate that the resultant model will prove to be a robust estimation strategy. Experimental simulations are carried out comparing the proposed approach with other conventional approaches on different varieties of functions corrupted by noises of varying distribution types.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".