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dc.contributor.authorPrasad, M.en_US
dc.contributor.authorLin, Y. Y.en_US
dc.contributor.authorLin, C. T.en_US
dc.contributor.authorEr, M. J.en_US
dc.contributor.authorPrasad, O. K.en_US
dc.date.accessioned2015-12-02T02:59:15Z-
dc.date.available2015-12-02T02:59:15Z-
dc.date.issued2015-11-01en_US
dc.identifier.issn0925-2312en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2015.04.034en_US
dc.identifier.urihttp://hdl.handle.net/11536/127968-
dc.description.abstractIn this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectNeural networksen_US
dc.subjectFuzzy systemen_US
dc.subjectBig-dataen_US
dc.subjectPrivacy and securityen_US
dc.subjectCollaborative techniqueen_US
dc.subjectOn-line learning systemen_US
dc.subjectTime series predictionen_US
dc.titleA new data-driven neural fuzzy system with collaborative fuzzy clustering mechanismen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.neucom.2015.04.034en_US
dc.identifier.journalNEUROCOMPUTINGen_US
dc.citation.volume167en_US
dc.citation.spage558en_US
dc.citation.epage568en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電機工程學系zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000358808500059en_US
dc.citation.woscount0en_US
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