Adaptive State Estimation and Continual Learning under Data Distribution Shift
Bibliographic record
Abstract
In state estimation, purely model-based and datadriven observers each have inherent limitations. In modelbased observers, finding an accurate mathematical model for complex dynamic systems is challenging. In contrast, data-driven methods often achieve superior accuracy under known conditions but exhibit poor generalization to unseen data distributions, particularly in dynamic and evolving environments where system behavior changes unpredictably. To overcome these challenges, we propose an adaptive hybrid estimator that dynamically switches between a model-based observer and a neural-network predictor. Additionally, it continually updates the neural network based observer parameters through continual learning as new data are observed, enabling robust adaptation to novel operating regions. To detect the distribution change in the input space for each individual data point, a memory selection strategy is used to store the most informative data points from historical data. The proposed approach is validated using experimental data from an electric Equinox vehicle under diverse driving scenarios. Results demonstrate that, compared to purely model-based or data-driven methods, the hybrid estimator significantly reduces estimation errors, thereby enhancing both accuracy and adaptability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".