Degree based models of granular computing under fuzzy indiscernibility relations

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 se...

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Autores principales:	Muhammad Akram, Ahmad N. Al-Kenani, Anam Luqman
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	fuzzy knowledge representation system granular structures fuzzy graphs degree based models comparative analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/14133974f0ec4eb499dc8338b54d93b4
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id	oai:doaj.org-article:14133974f0ec4eb499dc8338b54d93b4
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spelling	oai:doaj.org-article:14133974f0ec4eb499dc8338b54d93b42021-11-24T01:24:13ZDegree based models of granular computing under fuzzy indiscernibility relations10.3934/mbe.20214171551-0018https://doaj.org/article/14133974f0ec4eb499dc8338b54d93b42021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021417?viewType=HTMLhttps://doaj.org/toc/1551-0018The 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 (I). We describe the granular structures of I, including discernibility, core, reduct and essentiality of 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.Muhammad AkramAhmad N. Al-KenaniAnam Luqman AIMS Pressarticlefuzzy knowledge representation systemgranular structuresfuzzy graphsdegree based modelscomparative analysisBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8415-8443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy knowledge representation system granular structures fuzzy graphs degree based models comparative analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	fuzzy knowledge representation system granular structures fuzzy graphs degree based models comparative analysis Biotechnology TP248.13-248.65 Mathematics QA1-939 Muhammad Akram Ahmad N. Al-Kenani Anam Luqman Degree based models of granular computing under fuzzy indiscernibility relations
description	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 (I). We describe the granular structures of I, including discernibility, core, reduct and essentiality of 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.
format	article
author	Muhammad Akram Ahmad N. Al-Kenani Anam Luqman
author_facet	Muhammad Akram Ahmad N. Al-Kenani Anam Luqman
author_sort	Muhammad Akram
title	Degree based models of granular computing under fuzzy indiscernibility relations
title_short	Degree based models of granular computing under fuzzy indiscernibility relations
title_full	Degree based models of granular computing under fuzzy indiscernibility relations
title_fullStr	Degree based models of granular computing under fuzzy indiscernibility relations
title_full_unstemmed	Degree based models of granular computing under fuzzy indiscernibility relations
title_sort	degree based models of granular computing under fuzzy indiscernibility relations
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/14133974f0ec4eb499dc8338b54d93b4
work_keys_str_mv	AT muhammadakram degreebasedmodelsofgranularcomputingunderfuzzyindiscernibilityrelations AT ahmadnalkenani degreebasedmodelsofgranularcomputingunderfuzzyindiscernibilityrelations AT anamluqman degreebasedmodelsofgranularcomputingunderfuzzyindiscernibilityrelations
_version_	1718416020000997376

Degree based models of granular computing under fuzzy indiscernibility relations

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