Prediction error identification of Hammerstein models in the presence of ARIMA disturbances
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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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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 | 0.000 | 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 | 0.000 | 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