Simultaneous state‐estimator tuning and parameter estimation for systems with nonstationary disturbances, multi‐rate data, and measurement delays
Bibliographic record
Abstract
Abstract Model‐based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state‐estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time‐varying parameters, multi‐rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state‐estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum‐of‐squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more‐advanced state estimators that require the same tuning information as EKFs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".