A Type-3 Fuzzy Logic System with Uncertainty Bound Type-Reduction and Optimized Secondary Memberships and Level of Alpha-Cuts
Bibliographic record
Abstract
Abstract Recently interval type-3 (IT3) fuzzy logic systems (FLSs) are applied for various high-noisy problems. However, in most presented IT3-FLSs: (1) To convert the output T3 fuzzy sets (FSs) into a crisp value just the simple weighted average type-reductions are used that these approaches weakness the main concept of IT3-FSs; (2) The secondary memberships and number, rule format, number of FSs and $$\alpha $$ α -cuts are constant in existing IT3-FLSs; (3) One of the main properties of IT3-FSs is that the upper bound (UB) and lower bound (LB) of the footprint of uncertainty (FOU) are fuzzy numbers. However, in existing FSs, it is hard to determine an uncertainty bound for UB and LB of FOU. In this paper, new type-reduction and a new learning technique are introduced. The main contributions are as follows. (1) A type-reduction based on the theorem of uncertainty bounds is developed. The suggested method has no iterative computations, and it is much closer to the Karnik-Mendel technique. (2) A new type-3 (T3) fuzzy set with triangular secondary membership, and simple interval fuzzy bounds for UB and LB of FOU is introduced and formulated. (3) A new self-structuring technique based on Invasive Weed Optimization (IWO) is suggested for optimizing rule numbers, the format of rules, the level of $$\alpha $$ α -cuts, the secondary membership, the center of FSs, and the rule parameters. (4) By several simulations on modeling of real-world data, applied control applications, and statistical analyses, the effectiveness of the schemed FLS and learning strategy is verified.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".