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dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorPrasad, Mukeshen_US
dc.contributor.authorChang, Jyh-Yeongen_US
dc.date.accessioned2014-12-08T15:36:26Z-
dc.date.available2014-12-08T15:36:26Z-
dc.date.issued2013en_US
dc.identifier.isbn978-1-4799-0386-3en_US
dc.identifier.urihttp://hdl.handle.net/11536/24781-
dc.description.abstractThis 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.en_US
dc.language.isoen_USen_US
dc.subjectprivacy and securityen_US
dc.subjectfuzzy inference systemen_US
dc.subjectcollaborationen_US
dc.subjectfuzzy c-meansen_US
dc.subjectstructural learningen_US
dc.titleDesigning Mamdani Type Fuzzy Rule Using a Collaborative FCM Schemeen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2013 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY 2013)en_US
dc.citation.spage279en_US
dc.citation.epage282en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000339736400050-
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