A Type-3 Fuzzy Logic System with Uncertainty Bound Type-Reduction and Optimized Secondary Memberships and Level of Alpha-Cuts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -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 $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it