Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network
Bibliographic record
Abstract
Multiscale prediction analysis for the generation and consumption of by-product gas flows in various devices from the various production regions of the steel industry can be regarded as the prerequisite for energy scheduling and allocation. In this article, a generalized tensor granularity (GTG) based evolving interval type-2 (IT2) fuzzy neural network (GTG-EIT2FNN) is proposed to perform the multiscale prediction for spatio-temporal industrial data streams. A generalized IT2 fuzzy C-means clustering method is presented to extract the similarity characteristics from GTG that considers the spatial location, the semantics of manufacturing processes, the uncertainty triggered by multiple sensors, time-varying and multiscale property. Moreover, the robustness and adaptability of GTG-EIT2FNN is improved by incorporating an extended Q-learning to learn the optimal policy in terms of the input structure and network ones. A number of industrial study cases show that GTG-EIT2FNN outperforms state-of-the-art comparative algorithms in achieving the best tradeoff between accuracy and simplicity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".