Designing Mamdani type fuzzy rule using a collaborative FCM scheme

Lin, CT; Prasad, M; Chang, JY

Designing Mamdani type fuzzy rule using a collaborative FCM scheme

Lin, CT Prasad, M

Chang, JY

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: iFUZZY 2013 - 2013 International Conference on Fuzzy Theory and Its Applications, 2013, pp. 279-282
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Lin, CT
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Chang, JY
dc.date	2013-12-06
dc.date.accessioned	2023-03-24T10:38:54Z
dc.date.available	2023-03-24T10:38:54Z
dc.date.issued	2013-01-01
dc.identifier.citation	iFUZZY 2013 - 2013 International Conference on Fuzzy Theory and Its Applications, 2013, pp. 279-282
dc.identifier.isbn	9781479903863
dc.identifier.uri	http://hdl.handle.net/10453/168374
dc.description.abstract	This paper presents a new approach for generating fuzzy rules for fuzzy inference system by using collaborative fuzzy c-mean (CFCM). In order to do any mode of integration between datasets, there is a need to define the common feature between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling. This all consideration bring the concept of collaborative fuzzy rule generation process with a quality measuring. © 2013 IEEE.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	iFUZZY 2013 - 2013 International Conference on Fuzzy Theory and Its Applications
dc.relation.ispartof	International Conference on Fuzzy Theory and Its Applications (iFUZZY)
dc.relation.isbasedon	10.1109/iFuzzy.2013.6825450
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Designing Mamdani type fuzzy rule using a collaborative FCM scheme
dc.type	Conference Proceeding
utslib.location.activity	Taipei, TAIWAN
utslib.for	0902 Automotive Engineering
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	*
dc.date.updated	2023-03-24T10:38:53Z
pubs.finish-date	2013-12-08
pubs.publication-status	Published
pubs.start-date	2013-12-06

Abstract:

This paper presents a new approach for generating fuzzy rules for fuzzy inference system by using collaborative fuzzy c-mean (CFCM). In order to do any mode of integration between datasets, there is a need to define the common feature between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling. This all consideration bring the concept of collaborative fuzzy rule generation process with a quality measuring. © 2013 IEEE.

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http://hdl.handle.net/10453/168374