Prediction error identification of Hammerstein models in the presence of ARIMA disturbances
Bibliographic record
Abstract
In this paper, an algorithm is developed for the identification of a Hammerstein system in the presence of non-stationary measurement noise in the form of an Auto Regressive Integral Moving Average (ARIMA) model. Many systems used in the chemical process control industry can be modelled with the Hammerstein structure, a block oriented model consisting of a memoryless non-linearity followed by a linear filter. However, these systems are often subject to random step disturbances which violate the stationarity assumptions required by most system identification algorithms. Stationarity can be restored by differencing the measured output. As a result, parametric identification methods are applied to approximate the elements of the modified plant, and noise models, as well as the non-linearity simultaneously using prediction error minimization based approaches. Instrumental Variable methods are employed to generate good initial estimates of these systems, and so to decrease the chances of the optimization getting caught in suboptimal local minima. Estimates of the original system components are then recovered from the identified model. Monte-Carlo simulation and high-order correlation-based validation tests are used to demonstrate the performance of the 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".