Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers

Prasad, M; Chou, KP; Saxena, A; Kawrtiya, OP; Li, DL; Lin, CT

Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers

Prasad, M

Chou, KP Saxena, A Kawrtiya, OP Li, DL Lin, CT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICA 2014: 2014 IEEE Symposium on Computational Intelligence in Control and Automation, Proceedings, 2015, pp. 1-6
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Chou, KP
dc.contributor.author	Saxena, A
dc.contributor.author	Kawrtiya, OP
dc.contributor.author	Li, DL
dc.contributor.author	Lin, CT
dc.date	2014-12-09
dc.date.accessioned	2023-03-24T10:35:37Z
dc.date.available	2023-03-24T10:35:37Z
dc.date.issued	2015-01-01
dc.identifier.citation	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICA 2014: 2014 IEEE Symposium on Computational Intelligence in Control and Automation, Proceedings, 2015, pp. 1-6
dc.identifier.isbn	9781479945313
dc.identifier.uri	http://hdl.handle.net/10453/168371
dc.description.abstract	© 2014 IEEE. This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICA 2014: 2014 IEEE Symposium on Computational Intelligence in Control and Automation, Proceedings
dc.relation.ispartof	IEEE Symposium on Computational Intelligence in Control and Automation
dc.relation.isbasedon	10.1109/CICA.2014.7013227
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers
dc.type	Conference Proceeding
utslib.location.activity	USA
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-24T10:35:36Z
pubs.finish-date	2014-12-12
pubs.publication-status	Published
pubs.start-date	2014-12-09

Abstract:

© 2014 IEEE. This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.

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http://hdl.handle.net/10453/168371