A two‐layer temporal stochastic configuration broad learning system for complex industrial soft sensing
Bibliographic record
Abstract
Abstract Stochastic configuration broad learning system (SCBLS) has demonstrated notable advantages in industrial soft sensing due to its computational efficiency and supervised node configuration. However, conventional SCBLS overlooks the dynamic characteristics of industrial data, which limits the prediction accuracy of soft sensor models. To address this limitation, this study proposes a two‐layer temporal SCBLS (TT‐SCBLS) for industrial soft sensor modelling. The proposed framework consists of two key modules: an output‐related dual‐layer temporal feature extraction module and an enhancement node stochastic configuration module. The former is used to build the primary mapped features, where the first layer employs quality‐related slow feature analysis (QSFA) to extract intrinsic slow features, mitigating the impact of data noise, and the second layer utilizes a cycle reservoir with regular jumps (CRJ) network to capture temporal dependencies in the process data. The later introduces the stochastic configuration algorithm for incremental node expansion at the enhancement layer, ensuring efficient model adaptation. Additionally, a kernel Shapley additive explanation (SHAP) model is integrated to quantify the contributions of input variables, enhancing model transparency. The proposed method is validated on two benchmark industrial systems, with experimental results confirming its superior prediction performance compared to existing approaches.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".