Parameter Estimation in Continuous-Time Dynamic Models in the Presence of Unmeasured States and Nonstationary Disturbances
Bibliographic record
Abstract
Mathematical models that describe chemical engineering processes are not exact. Therefore, it is important to develop parameter-estimation algorithms that account for possible model uncertainties. In this article, as a follow-up to earlier work by Poyton et al. ( Comput. Chem. Eng. 2006, 30, 698) and Varziri et al. ( Comput. Chem. Eng. 2007 ), we investigate the performance of an approximate maximum likelihood estimation (AMLE) algorithm for parameter estimation in nonlinear dynamic models with model uncertainties and stochastic disturbances. We examine the applicability of AMLE to cases in which some of the states are unmeasured, and we demonstrate that AMLE can be employed in models with nonstationary process disturbances. Theoretical confidence interval expressions are obtained and are compared to empirical box plots from Monte Carlo simulations. Use of the methodology is illustrated using a continuous stirred tank reactor model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".