Offline identification and output prediction for a class of <scp>SISO W</scp> iener process
Bibliographic record
Abstract
Abstract In this paper, a simplified Wiener structure (SWS) for single‐input‐single‐output (SISO) Wiener processes and an identification method based on Gaussian mixture model (GMM) and expectation maximization (EM) algorithm are proposed. The vast majority of industrial processes can be regarded as approximately monotonic non‐linear processes. Approximately, a monotonic characteristic is introduced into the non‐linear module and a rich dynamic characteristic is added into the linear module of the Wiener model. Thus, SWS is exploited and has strong generalization ability. Because GMM can describe the arbitrary distribution of sample data in theory, it is used to accurately describe the output data, including the system error term of SWS. Hence, a statistical model (Equation (17)) is obtained. Then, the EM algorithm is introduced to identify the parameters of the statistical model that contains the parameters of SWS. In the end, two numerical examples demonstrate the effectiveness of both the SWS and the GMM‐EM‐based iterative offline identification algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".