Identification of nonlinear parameter varying systems with missing output data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract An identification of nonlinear parameter varying systems using particle filter under the framework of the expectation‐maximizaiton (EM) algorithm is described. In chemical industries, processes are often designed to perform tasks under various operating conditions. To circumvent the modeling difficulties rendered by multiple operating conditions and the transitions between different working points, the EM algorithm, which iteratively increases the likelihood function, is applied. Meanwhile the missing output data problem which is common in real industry is also considered in this work. Particle filters are adopted to deal with the computation of expectation functions. The efficiency of the proposed method is illustrated through simulated examples and a pilot‐scale experiment. © 2012 American Institute of Chemical Engineers AIChE J, 2012
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it