Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/73812
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dc.contributorSchool of Nursingen_US
dc.creatorZhang, Jen_US
dc.creatorDeng, Zen_US
dc.creatorChoi, Ken_US
dc.creatorWang, Sen_US
dc.date.accessioned2018-03-29T07:15:24Z-
dc.date.available2018-03-29T07:15:24Z-
dc.identifier.issn1063-6706en_US
dc.identifier.urihttp://hdl.handle.net/10397/73812-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication J. Zhang, Z. Deng, K. Choi and S. Wang, "Data-Driven Elastic Fuzzy Logic System Modeling: Constructing a Concise System With Human-Like Inference Mechanism," in IEEE Transactions on Fuzzy Systems, vol. 26, no. 4, pp. 2160-2173, Aug. 2018 is available at http://doi.org/10.1109/TFUZZ.2017.2767025.en_US
dc.subjectConcise and interpretable modelen_US
dc.subjectElastic fuzzy logic systemsen_US
dc.subjectHigh-dimensional dataen_US
dc.subjectTSK fuzzy logic systemen_US
dc.titleData-driven elastic fuzzy logic system modeling : constructing a concise system with human-like inference mechanismen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2160en_US
dc.identifier.epage2173en_US
dc.identifier.volume26en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1109/TFUZZ.2017.2767025en_US
dcterms.abstractThe construction of fuzzy logic systems (FLSs) using data-driven techniques has become the most popular modeling approach. However, this approach still faces critical challenges, including the difficulty in obtaining concise models for high-dimensional data and generating accurate fuzzy rules to simulate human inference mechanism. To tackle these issues, a new FLS modeling framework called data-driven elastic FLS (DD-EFLS) is proposed in this paper. The DD-EFLS has two key characteristics. First, the fuzzy rules in the rule base can use different feature subspaces that are extracted from the original high-dimensional space to yield simple and accurate rules in feature spaces of lower dimensionality. Second, fuzzy inferences from various views are implemented by embedding different rules in the corresponding subspaces to imitate human inference mechanism. Based on the DD-EFLS framework, an elastic Takagi-Sugeno-Kang (TSK) FLS modeling method (ETSK-FLS) is proposed to train the elastic TSK FLS using the concise rules and a more human-like inference mechanism for modeling tasks based on high-dimensional datasets. The characteristics and advantages of the proposed framework and the ETSK-FLS method are validated experimentally using both synthetic and real-world datasets.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on fuzzy systems, Aug. 2017, v. 26, no. 4, p. 2160-2173en_US
dcterms.isPartOfIEEE transactions on fuzzy systemsen_US
dcterms.issued2017-08-
dc.identifier.scopus2-s2.0-85032455330-
dc.identifier.eissn1941-0034en_US
dc.description.validate201802 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0597-n18-
dc.identifier.SubFormID458-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU 152040/16Een_US
dc.description.pubStatusPublisheden_US
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