A novel fuzzy system modeling approach: multidimensional structure identification and inference
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
A new fuzzy system modeling approach that uses an inference mechanism working in the input-output space is proposed. The new inference mechanism eliminates the need to identify the membership functions on each separate system variable axis and avoids the problems due to the projection step of some popular fuzzy system modeling approaches. In the new method, inputs and outputs are first clustered together by means of the fuzzy c-means (FCM) algorithm, with several levels of fuzziness, m, and numbers of clusters, c. Instead of a cluster validity index, the system output error is used as our performance index while selecting the best (m,c) pairs. Then, a modified version of the classical simulated annealing algorithm is used to identify the relative weights of the system input variables.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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