Multi-granulation hesitant fuzzy rough sets and corresponding applications
This paper develops a single-granulation hesitant fuzzy rough set (SGHFRS) model from the perspective of granular computing. In the multi-granulation framework, we propose two types of multi-granulation rough sets model called the optimistic multi-granulation hesitant fuzzy rough sets (OMGHFRSs) and...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2019-12, Vol.23 (24), p.13085-13103 |
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| creator | Zhang, Haidong Zhan, Jianming He, Yanping |
| description | This paper develops a single-granulation hesitant fuzzy rough set (SGHFRS) model from the perspective of granular computing. In the multi-granulation framework, we propose two types of multi-granulation rough sets model called the optimistic multi-granulation hesitant fuzzy rough sets (OMGHFRSs) and pessimistic multi-granulation hesitant fuzzy rough sets (PMGHFRSs). In the models, the multi-granulation hesitant fuzzy lower and upper approximations are defined based on multiple hesitant fuzzy tolerance relations. The relationships among the SGHFRSs, OMGHFRSs and PMGHFRSs are also established. In order to further measure the uncertainty of multi-granulation hesitant fuzzy rough sets (MGHFRSs), the concepts of rough measure and rough measure about the parameters
α
and
β
are presented and some of their interesting properties are examined. Finally, we give a decision-making method based on the MGHFRSs, and the validity of this approach is illustrated by two practical applications. Compared with the existing results, we also expound its advantages. |
| doi_str_mv | 10.1007/s00500-019-03853-3 |
| format | Article |
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α
and
β
are presented and some of their interesting properties are examined. Finally, we give a decision-making method based on the MGHFRSs, and the validity of this approach is illustrated by two practical applications. Compared with the existing results, we also expound its advantages.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-019-03853-3</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Approximation ; Artificial Intelligence ; Computational Intelligence ; Control ; Decision making ; Engineering ; Fuzzy sets ; Granulation ; Information systems ; Mathematical Logic and Foundations ; Mechatronics ; Methodologies and Application ; Morality ; Robotics ; Set theory ; Students</subject><ispartof>Soft computing (Berlin, Germany), 2019-12, Vol.23 (24), p.13085-13103</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-3943e12224136077d78a4466e734c4fe487c9f62a1c3937ee579b23263b8f8233</citedby><cites>FETCH-LOGICAL-c367t-3943e12224136077d78a4466e734c4fe487c9f62a1c3937ee579b23263b8f8233</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-019-03853-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918048634?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Zhang, Haidong</creatorcontrib><creatorcontrib>Zhan, Jianming</creatorcontrib><creatorcontrib>He, Yanping</creatorcontrib><title>Multi-granulation hesitant fuzzy rough sets and corresponding applications</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>This paper develops a single-granulation hesitant fuzzy rough set (SGHFRS) model from the perspective of granular computing. In the multi-granulation framework, we propose two types of multi-granulation rough sets model called the optimistic multi-granulation hesitant fuzzy rough sets (OMGHFRSs) and pessimistic multi-granulation hesitant fuzzy rough sets (PMGHFRSs). In the models, the multi-granulation hesitant fuzzy lower and upper approximations are defined based on multiple hesitant fuzzy tolerance relations. The relationships among the SGHFRSs, OMGHFRSs and PMGHFRSs are also established. In order to further measure the uncertainty of multi-granulation hesitant fuzzy rough sets (MGHFRSs), the concepts of rough measure and rough measure about the parameters
α
and
β
are presented and some of their interesting properties are examined. Finally, we give a decision-making method based on the MGHFRSs, and the validity of this approach is illustrated by two practical applications. Compared with the existing results, we also expound its advantages.</description><subject>Approximation</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Decision making</subject><subject>Engineering</subject><subject>Fuzzy sets</subject><subject>Granulation</subject><subject>Information systems</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methodologies and Application</subject><subject>Morality</subject><subject>Robotics</subject><subject>Set theory</subject><subject>Students</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kD1PwzAURS0EEqXwB5giMRue_Rw7HlHFp4pYYLbc1ElTBTvYydD-etIGiY3p3eGe-6RDyDWDWwag7hJADkCBaQpY5EjxhMyYQKRKKH16zJwqKfCcXKS0BeBM5Tgjr29D2ze0jtYPre2b4LONS01vfZ9Vw36_y2IY6k2WXJ8y69dZGWJ0qQt-3fg6s13XNuWRS5fkrLJtcle_d04-Hx8-Fs90-f70srhf0hKl6ilqgY5xzgVDCUqtVWGFkNIpFKWonChUqSvJLStRo3IuV3rFkUtcFVXBEefkZtrtYvgeXOrNNgzRjy8N16wAUUgUY4tPrTKGlKKrTBebLxt3hoE5ODOTMzM6M0dn5jCNE5TGsq9d_Jv-h_oBN91ucw</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Zhang, Haidong</creator><creator>Zhan, Jianming</creator><creator>He, Yanping</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20191201</creationdate><title>Multi-granulation hesitant fuzzy rough sets and corresponding applications</title><author>Zhang, Haidong ; Zhan, Jianming ; He, Yanping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-3943e12224136077d78a4466e734c4fe487c9f62a1c3937ee579b23263b8f8233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Approximation</topic><topic>Artificial Intelligence</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Decision making</topic><topic>Engineering</topic><topic>Fuzzy sets</topic><topic>Granulation</topic><topic>Information systems</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Methodologies and Application</topic><topic>Morality</topic><topic>Robotics</topic><topic>Set theory</topic><topic>Students</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Haidong</creatorcontrib><creatorcontrib>Zhan, Jianming</creatorcontrib><creatorcontrib>He, Yanping</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Haidong</au><au>Zhan, Jianming</au><au>He, Yanping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-granulation hesitant fuzzy rough sets and corresponding applications</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2019-12-01</date><risdate>2019</risdate><volume>23</volume><issue>24</issue><spage>13085</spage><epage>13103</epage><pages>13085-13103</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>This paper develops a single-granulation hesitant fuzzy rough set (SGHFRS) model from the perspective of granular computing. In the multi-granulation framework, we propose two types of multi-granulation rough sets model called the optimistic multi-granulation hesitant fuzzy rough sets (OMGHFRSs) and pessimistic multi-granulation hesitant fuzzy rough sets (PMGHFRSs). In the models, the multi-granulation hesitant fuzzy lower and upper approximations are defined based on multiple hesitant fuzzy tolerance relations. The relationships among the SGHFRSs, OMGHFRSs and PMGHFRSs are also established. In order to further measure the uncertainty of multi-granulation hesitant fuzzy rough sets (MGHFRSs), the concepts of rough measure and rough measure about the parameters
α
and
β
are presented and some of their interesting properties are examined. Finally, we give a decision-making method based on the MGHFRSs, and the validity of this approach is illustrated by two practical applications. Compared with the existing results, we also expound its advantages.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-019-03853-3</doi><tpages>19</tpages></addata></record> |
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| subjects | Approximation Artificial Intelligence Computational Intelligence Control Decision making Engineering Fuzzy sets Granulation Information systems Mathematical Logic and Foundations Mechatronics Methodologies and Application Morality Robotics Set theory Students |
| title | Multi-granulation hesitant fuzzy rough sets and corresponding applications |
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