Adaptive Estimation Using Interacting Multiple Model With Moving Window
Bibliographic record
Abstract
State estimation is paramount in control, monitoring, and fault management across various domains. Uncertainty in model parameters and changing system dynamics pose significant challenges to accurate state estimation. This paper proposes a novel adaptive estimation strategy called the Moving Window Interacting Multiple Model (MWIMM). Using a moving window improves identifiability and computational efficiency of the multiple model algorithms by focusing on a subset of possible models, rather than considering all models at each stage. MWIMM enables the estimation of gradual changes in the system, making it valuable for fault intensity and Remaining Useful Life (RUL) estimation. The paper provides an overview of adaptive estimation strategies, presents the formulation of MWIMM for fault intensity and RUL estimation, and investigates the parameter estimation problem. Results are compared with those of augmented state Extended Kalman Filter (EKF) estimation, and it is shown that the proposed MWIMM approach offers a promising alternative for effectively handling extensive parameter uncertainty and accommodating gradual changes in system parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".