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. Designing an autonomous and robust FSM and reasoning with it is the prime goal of this approach. This new FSM approach implements higher-level fuzzy sets to identify the uncertainties in: (1) the system parameters, and (2) the structure of “Fuzzy Functions”. With the identification of these parameters, an interval valued fuzzy sets and “Fuzzy Functions” are identified. Finally, an evolutionary computing approach with the proposed uncertainty identification strategy is combined to build FSMs that can automatically identify these uncertainty intervals. After testing proposed FSM tool on various benchmark problems, the algorithms are successfully applied to model decision processes in two real problem domains: desulphurization process in steel making and stock price prediction activities. For both problems, the proposed methods produce robust and high performance models, which are comparable (if not better) than the best system modeling approaches known in current literature. Several aspects of the proposed methodologies are thoroughly analyzed to provide a deeper understanding. These analyses show consistency of the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".