Open AccessDegree based models of granular computing under fuzzy indiscernibility relations
Author(s) -
Muhammad Akram,
Ahmad N. Al-Kenani,
Anam Luqman
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021417
Subject(s) - granular computing , vagueness , fuzzy logic , relation (database) , granulation , mathematics , fuzzy set , representation (politics) , fuzzy set operations , reduct , computer science , data mining , artificial intelligence , theoretical computer science , rough set , engineering , geotechnical engineering , politics , law , political science
The aim of this research work is to put forward fuzzy models of granular computing based on fuzzy relation and fuzzy indiscernibility relation. Thanks to fuzzy information granulation to provide multi-level visualization of problems that include uncertain information. In such a granulation, fuzzy sets and fuzzy graphs help us to represent relationships among granules, groups or clusters. We consider the fuzzy indiscernibility relation of a fuzzy knowledge representation system ($ \mathcal{I} $). We describe the granular structures of $ \mathcal{I} $, including discernibility, core, reduct and essentiality of $ \mathcal{I} $. Then we examine the contribution of these structures to granular computing. Moreover, we introduce certain granular structures using fuzzy graph models and discuss degree based model of fuzzy granular structures. Granulation of network models based on fuzzy information effectively handles real life data which possesses uncertainty and vagueness. Finally, certain algorithms of proposed models are developed and implemented to solve real life problems involving uncertain granularities. We also present a concise comparison of the models developed in our work with other existing methodologies.