.png){kind=icon}
Return
Information Granularity in Fuzzy Binary GrC Model
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2010.2095461.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Zadeh's seminal work in theory of fuzzy-information granulation in human reasoning is inspired by the ways in which humans granulate information and reason with it. This has led to an interesting research topic: granular computing (GrC). Although many excellent research contributions have been made, there remains an important issue to be addressed: What is the essence of measuring a fuzzy-information granularity of a fuzzy-granular structure? What is needed to answer this question is an axiomatic constraint with a partial-order relation that is defined in terms of the size of each fuzzy-information granule from a fuzzy-binary granular structure. This viewpoint is demonstrated for fuzzy-binary granular structure, which is called the binary GrC model by Lin. We study this viewpoint from from five aspects in this study, which are fuzzy BINARY-granular-structure operators, partial-order relations, measures for fuzzy-information granularity, an axiomatic approach to fuzzy-information granularity, and fuzzy-information entropies.
Keywords:
Fuzzy-information entropy
fuzzy-information granularity
granular computing (GrC)
partial-order relation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.9
Papers:
5.0K
Citations:
2.9W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveLate gadolinium enhancement on cardiac magnetic resonance combined with 123I- metaiodobenzylguanidine scintigraphy strongly predicts long-term clinical outcome in patients with dilated cardiomyopathy.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSave
View more