A systematic method of adaptive fuzzy logic modeling, using an improved fuzzy c-means clustering algorithm for rule generation
Why this work is in the frame
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Bibliographic record
Abstract
Complex dynamical systems, which are difficult to be mathematically modeled, can be described by a fuzzy model. This paper attempts to improve and to address the problems concerning the systematic fuzzy-logic modeling, by introducing the following concepts: 1) an effective theoretical base method to identify the optimum fuzziness parameter (weighting exponent) m instead of the heuristic selection method mainly reported in the literature; 2) an additional criterion to choose the optimum number of clusters (rules) using fuzzy model output variation with number of clusters; 3) a generalized and parameterized reasoning mechanism constructed based on the weighted sum of the normalized defuzzified output value of each individual rule. Fuzzy model with this reasoning mechanism is suitable for online learning and real-time control applications; and 4) a gradient-descent based parameter adjustment to tune the parameters of reasoning mechanism instead of the existing heuristic parameter identification in the literature. The proposed systematic method of fuzzy modeling has the advantages of simplicity, flexibility, and high accuracy. The two example data, which have been widely used in the textbooks and literature as benchmark, are used to evaluate the performance of the proposed method
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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 | 0.001 |
| 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