Multiple-Model Based Linear Parameter Varying Time-Delay System Identification with Missing Output Data Using an Expectation-Maximization Algorithm
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Bibliographic record
Abstract
This paper is concerned with the identification problems of the linear parameter varying (LPV) system with missing output in the presence of the time-delay. A multiple-model approach is adopted. Local models varying from one operating point to another are first described by finite impulse response (FIR) models. To handle missing output and time-delay, the expectation-maximization (EM) algorithm is utilized to estimate the unknown parameters and the time-delay simultaneously. Output Error (OE) models are widely used in controller design. Therefore, the auxiliary model principle is employed to recover the OE models based on the initially identified FIR models. The EM algorithm is then used again to refine the unknown parameters of the OE models with the complete data set to obtain the final global model. Simulation examples are presented to demonstrate the performance of the proposed method.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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