T3-ANFIS: Type-3 Adaptive Neuro-Fuzzy Inference System With a Noniterative Learning Algorithm
Bibliographic record
Abstract
Recently, type-3 (T3) fuzzy logic systems (FLSs) have been widely used in various problems, such as modeling, control systems, image processing, forecasting problems, optimization algorithms, and many others. Most studies of T3-FLS focus on its different applications. However, the basic theory, the applications in real-time and online problems, learning schemes, and the robustness against non-Gaussian noises have been rarely studied. In this article, the simplification of T3-FLSs is taken into account, and the new membership functions (MFs), learning schemes, and type reduction are introduced. The concept of singleton MFs in adaptive fuzzy inference systems (ANFIS) is extended to T3-FLSs, and T3-ANFIS is proposed. The type reduction is simplified, and a noniterative learning scheme is developed. The corresponding computations for adaptation laws are derived, and all rules parameters and MF parameters are adjusted. To enhance the robustness versus impulsive noises, a T3-FLS-based correntropy Kalman filter (CKF) is designed. In the suggested algorithm, the kernel-size is not a constant value, but it is online updated by a T3-FLS. Also, to further improve robustness against noisy data, nonsingleton fuzzification for the suggested MF is formulated. By several simulations using real data sets, the feasibility of the suggested T3-FLS is shown, and its superiority is verified by comparisons. Also, the better robustness of suggested T3-FLS-based CKF versus impulsive noises is shown by comparison with traditional KFs.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".