Dual Updating Strategy for Moving-Window Partial Least-Squares Based on Model Performance Assessment
Bibliographic record
Abstract
Partial least-squares (PLS) is a popular method for the development of data-driven models which in general are locally valid. To account for the time-varying properties of a process and to maintain the performance of a PLS model, the model needs to be updated regularly. In order to reduce the high model updating frequency (which leads to a heavy computational load) in typical adaptive modeling methods, in this work, a model performance assessment method is proposed to detect the significant model degradation, upon which the model updating is activated. Subsequently, a dual updating strategy based on the model performance assessment method is proposed for a moving-window PLS model in an attempt to effectively track the time-varying behavior of a process. In the dual updating strategy, model updating and bias updating are activated alternatively based on the results of the model performance assessment. To illustrate the effectiveness of the proposed approach, the dual updating method is applied to two industrial processes. The simulation results based on real industrial data demonstrate that the method can significantly reduce the model updating frequency while maintaining the prediction accuracy of the model.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".