Name
Abstract | ||
|---|---|---|
This paper proposes algorithms to construct fuzzy probabilities to represent or model the mixed aleatory and epistemic uncertainty in a limited-size ensemble. Specifically, we discuss the possible requirements for the fuzzy probabilities in order to model the mixed types of uncertainty, and propose algorithms to construct fuzzy probabilities for both independent and dependent datasets. The effectiveness of the proposed algorithms is demonstrated using one-dimensional and high-dimensional examples. After that, we apply the proposed uncertainty representation technique to isocontour extraction, and demonstrate its applicability using examples with both structured and unstructured meshes. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.ijar.2015.07.002 | International Journal of Approximate Reasoning |
Keywords | Field | DocType |
Aleatory uncertainty,Epistemic uncertainty,Fuzzy set,Uncertainty modeling,Marching cubes algorithm,Isocontour extraction | Uncertainty quantification,Polygon mesh,Marching cubes,Fuzzy logic,Algorithm,Sensitivity analysis,Uncertainty analysis,Fuzzy set,Artificial intelligence,Uncertainty representation,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
66 | 1 | 0888-613X |
Citations | PageRank | References |
4 | 0.41 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanyan He | 1 | 217 | 25.64 |
| Mahsa Mirzargar | 2 | 129 | 7.87 |
| Robert M. Kirby | 3 | 1443 | 115.55 |