An Iterative Algebraic Geometric Approach for Identification of Switched ARX Models with Noise
Bibliographic record
Abstract
Abstract The algebraic geometric (AG) approach has been used to identify switched auto regressive exogenous (SARX) models in hybrid systems, and it has several advantages over other SARX identification methods. This paper is focused on improving the estimation accuracy of the AG approach for systems corrupted with indispensable noises. A stochastic hybrid decoupling polynomial (SHDP) is constructed by reformulating the hybrid decoupling polynomial (HDP) used in the original AG method. An iterative scheme is developed to estimate parameters of the SHDP, which are used to calculate the SARX model parameters. This estimation involves linear regression with multiplicative noises, therefore a novel approach is proposed to solve this regression problem. Then, the parameters are recovered from the SHDP. Finally, all these steps for SARX model identification are summarized in an algorithm called the iterative algebraic geometric (IAG) approach. Simulations and experimental validation results are shown to demonstrate the effectiveness of and the improvement made by the proposed IAG 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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".