A Novel Data‐Driven Bilinear Subspace Identification Approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Subspace identification methods for bilinear systems perform computation with data matrix exploding. Huge computational burdens have been the biggest problem that prohibits real applications of bilinear subspace identification. In this paper, we propose a novel approach with the identification of bilinear predictor model from input‐output data with enhanced computational efficiency. Based on the displacement structure theory, the QR factorization is replaced with a fast Cholesky factorization, which deals with the curse of huge dimensionality and therefore reduces the computation cost. These improvements make the bilinear subspace approach more computationally efficient with good prediction ability. Finally, the proposed control approach is illustrated with a simulation of the non‐linear continuously stirred tank reactor (CSTR) system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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