Development of adaptive moving window <scp>LSTM</scp> ‐ <scp>EKF</scp> state and parameter estimator through maximum likelihood framework
Bibliographic record
Abstract
Abstract For efficient control, monitoring, and online optimization of any processes, accurate information of the key process variables is critical. Data driven soft‐sensors, which can predict the key quality variables using historical data, have gained significant attention in the process industry in the recent years. These soft‐sensors are generally developed under normal operating conditions. In reality, chemical processes are subjected to abnormalities such as process parameter drift, sensor bias, or unmeasured disturbances. Under these abnormal conditions, the performance of data‐driven soft sensors developed under normal data conditions will deteriorate over a period of time. To account for these data abnormality issues, soft‐sensor models need to be adapted and robustified. In this work, a dynamic recurrent neural network‐based state and parameter estimator is developed through moving window maximum likelihood framework. Initially, a machine learning state estimator is developed by integrating long short‐term memory (LSTM) networks with the extended Kalman filter (EKF), which can estimate the critical quality variable using real‐time process measurements. To address the data quality issues such as process drift and sensor bias, in the presence of measurement delays, an innovation error‐based moving window maximum likelihood approach state and parameter estimator is proposed, where the LSTM integrated EKF is used as the state estimator. The performance of the proposed state and parameter estimator is evaluated on benchmark systems. From the analysis of results, it is observed that the proposed machine learning‐based state and parameter estimator accurately estimates the states in presence of data abnormalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".