A New Distance for Intuitionistic Fuzzy Sets Based on Similarity Matrix

Cheng, C; Xiao, F; Cao, Z

A New Distance for Intuitionistic Fuzzy Sets Based on Similarity Matrix

Cheng, C Xiao, F Cao, Z

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 70436 - 70446
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Cheng, C	en_US
dc.contributor.author	Xiao, F	en_US
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 70436 - 70446	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135841
dc.description.abstract	© 2013 IEEE. Measuring the distance between two intuitionistic fuzzy sets (IFSs) is an open issue. Many types of distances for the IFSs have been proposed in previous studies. Some existing methods cannot satisfy the axioms of similarity or provide counterintuitive cases. Others ignore the relationship between three parameters characterizing the information carried by the IFS. To address these issues, a new distance is proposed by analyzing the similarity among the three parameters of the IFS. The comparison with some existing distances illustrates that the new distance has a higher sensitivity and can effectively measure the similarity between the IFSs. The results of the application of pattern recognition are also shown that the proposed method has better recognition ability.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2919521	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A New Distance for Intuitionistic Fuzzy Sets Based on Similarity Matrix	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2013 IEEE. Measuring the distance between two intuitionistic fuzzy sets (IFSs) is an open issue. Many types of distances for the IFSs have been proposed in previous studies. Some existing methods cannot satisfy the axioms of similarity or provide counterintuitive cases. Others ignore the relationship between three parameters characterizing the information carried by the IFS. To address these issues, a new distance is proposed by analyzing the similarity among the three parameters of the IFS. The comparison with some existing distances illustrates that the new distance has a higher sensitivity and can effectively measure the similarity between the IFSs. The results of the application of pattern recognition are also shown that the proposed method has better recognition ability.

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http://hdl.handle.net/10453/135841