Higher order fuzzy system identification using subtractive clustering
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a higher order fuzzy system identification method using subtractive clustering, which is an extended application of subtractive clustering. Minimum error models are obtained through enumerative search of clustering parameters. The results of the enumerative study presented in this paper explain the mechanism behind subtractive clustering and introduce a modification in the penalizing process of subtractive clustering. The results of applying the higher order modeling to both linear and non-linear systems are given in this paper. The comparison with the results of other models shows improvement in the modeling performance by subtractive clustering with higher order system identification technique. The higher order identification method resulted in fewer rules compared to lower order models. Results of case studies on systems of different complexities are also presented.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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