Type-2 fuzzy rule base system with parameter optimization for forecasting of tardiness
Bibliographic record
Abstract
This paper addresses an interval type-2 fuzzy hybrid rule-based system in order to predict the amount of tardiness where tardiness variables are represented by interval type-2 membership functions. For this purpose, interval type-2 fuzzy disjunctive normal forms and fuzzy conjunctive normal forms are utilized in the inference engine. The main contribution of this paper is to present the interval type-2 fuzzy hybrid rule-based system, which is the combination of Mamdani and Sugeno methods. In order to forecast the future amount of tardiness for continuous casting operation in a steel company in Canada, an autoregressive moving average model is used in the consequents of the rules. Parameters of the system are optimized by applying Adaptive-Network-Based Fuzzy Inference System (ANFIS). This method is compared with interval type-2 fuzzy Takagi-Sugeno-Kang method in MATLAB, multiple-regression, and two other Type-1 fuzzy methods in literature. The results of computing the mean square error of these methods show that our proposed method has less error and high accuracy in comparison with other methods.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".