Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30012
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dc.contributorDepartment of Computingen_US
dc.contributorSchool of Nursingen_US
dc.creatorDeng, Zen_US
dc.creatorJiang, Yen_US
dc.creatorChoi, KSen_US
dc.creatorChung, FLen_US
dc.creatorWang, Sen_US
dc.date.accessioned2015-07-14T01:32:19Z-
dc.date.available2015-07-14T01:32:19Z-
dc.identifier.issn2162-237Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/30012-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2013 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 Z. Deng, Y. Jiang, K. Choi, F. Chung and S. Wang, "Knowledge-Leverage-Based TSK Fuzzy System Modeling," in IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 8, pp. 1200-1212, Aug. 2013 is available at https://doi.org/10.1109/TNNLS.2013.2253617.en_US
dc.subjectFuzzy modelingen_US
dc.subjectFuzzy systems (FS)en_US
dc.subjectKnowledge leverage (KL)en_US
dc.subjectMissing dataen_US
dc.subjectTransfer learningen_US
dc.titleKnowledge-leverage-based TSK fuzzy system modelingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1200en_US
dc.identifier.epage1212en_US
dc.identifier.volume24en_US
dc.identifier.issue8en_US
dc.identifier.doi10.1109/TNNLS.2013.2253617en_US
dcterms.abstractClassical fuzzy system modeling methods consider only the current scene where the training data are assumed to be fully collectable. However, if the data available from the current scene are insufficient, the fuzzy systems trained by using the incomplete datasets will suffer from weak generalization capability for the prediction in the scene. In order to overcome this problem, a knowledge-leverage-based fuzzy system (KL-FS) is studied in this paper from the perspective of transfer learning. The KL-FS intends to not only make full use of the data from the current scene in the learning procedure, but also effectively leverage the existing knowledge from the reference scenes. Specifically, a knowledge-leverage-based Takagi-Sugeno-Kang-type Fuzzy System (KL-TSK-FS) is proposed by integrating the corresponding knowledge-leverage mechanism. The new fuzzy system modeling technique is evaluated through experiments on synthetic and real-world datasets. The results demonstrate that KL-TSK-FS has better performance and adaptability than the traditional fuzzy modeling methods in scenes with insufficient data.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on neural networks and learning systems, Aug. 2013, v. 24, no. 8, p. 1200-1212en_US
dcterms.isPartOfIEEE transactions on neural networks and learning systemsen_US
dcterms.issued2013-08-
dc.identifier.isiWOS:000322039500003-
dc.identifier.scopus2-s2.0-84880920607-
dc.identifier.eissn2162-2388en_US
dc.identifier.rosgroupidr67768-
dc.description.ros2013-2014 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0597-n14-
dc.identifier.SubFormID454-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU5134/12Een_US
dc.description.pubStatusPublisheden_US
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