A variational Bayesian approach to identification of switched ARX models
Bibliographic record
Abstract
In the identification of switched Auto-Regressive eXogenous (SARX) models, the number of local models is often assumed to be known a priori. However, in many industrial applications the prior process knowledge or the available information about the plant operation might not be sufficient to determine the number of local models. In such cases, the optimal number of local models needs to be inferred from collected operational data. The switching mechanism of the process is also often unknown. Therefore, classical SARX identification methods assuming a piecewise affine system fail to accurately identify randomly switched models. Furthermore, classical identification methods result in single-point estimates of unknown parameters, thereby ignoring the parameter uncertainty. The main objective of this work is to formulate and solve the problem of SARX model identification under the variational Bayesian framework through which the aforementioned challenging issues can be addressed. As a full Bayesian system identification approach, the proposed method not only provides a posterior distribution over model parameters to reveal the level of uncertainty of the estimated values, but also determines the optimal number of local models automatically. Since the identification pair identity at each sampling instant can be inferred from the data set, the switching mechanism will not influence the identification results. The effectiveness of the proposed Bayesian approach is demonstrated through a simulation case study.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".