Modeling uncertainty with evolutionary improved "fuzzy functions"
Bibliographic record
Abstract
Fuzzy system modeling (FSM)--meaning the construction of a representation of fuzzy systems models--is a difficult task. It demands an identification of many parameters. This thesis analyses fuzzy-modeling problems and different approaches to cope with it. It focuses on a novel evolutionary FSM approach--the design of "Improved Fuzzy Functions" system models with the use of evolutionary algorithms. In order to promote this analysis, local structures are identified with a new improved fuzzy clustering method and represented with novel "fuzzy functions". The central contribution of this work is the use of evolutionary algorithms--in particular, genetic algorithms--to find uncertainty interval of parameters to improve "Fuzzy Function" models. To replace the standard fuzzy rule bases (FRBs) with the new "Improved Fuzzy Functions" succeeds in capturing essential relationships in structure identification processes and overcomes limitations exhibited by earlier FRB methods because there are abundance of fuzzy operations and hence the difficulty of the choice of amongst the t-norms and co-norms.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".