A Data-Based Augmented Model Identification Method for Linear Errors-in-Variables Systems Based on EM Algorithm
Bibliographic record
Abstract
With a large amount of industrial data available, it is of considerable interest to develop data-based models. The challenge lies in the significant noises that appear in all data collected from industry. The errors-in-variables (EIV) model is a model that accounts for measurement noises in all observations (both input and output). In most of the traditional EIV identification methods, the input generation dynamics is not considered. In this paper, a dynamic model is applied to describe the input generation process, and then, the Kalman smoother is used to estimate its state using all available measurements. In order to utilize all of the observed variables in the EIV process, an augmented EIV model is derived to describe both input generation process and the EIV process dynamics itself. The parameters in the EIV model are then estimated by applying an expectation maximization algorithm. Simulated numerical example and an experiment performed on a hybrid tank system are used to demonstrate the improved identification performance of the proposed method.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".