Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66600
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dc.contributorSchool of Nursingen_US
dc.creatorYang, CJen_US
dc.creatorDeng, ZHen_US
dc.creatorChoi, KSen_US
dc.creatorWang, STen_US
dc.date.accessioned2017-05-22T02:26:26Z-
dc.date.available2017-05-22T02:26:26Z-
dc.identifier.issn1063-6706en_US
dc.identifier.urihttp://hdl.handle.net/10397/66600-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2015 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 C. Yang, Z. Deng, K. Choi and S. Wang, "Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals," in IEEE Transactions on Fuzzy Systems, vol. 24, no. 5, pp. 1079-1094, Oct. 2016 is available at http://dx.doi.org/10.1109/TFUZZ.2015.2501438.en_US
dc.subjectDistribution diversityen_US
dc.subjectElectroencephalogram (EEG)en_US
dc.subjectEpilepsy detectionen_US
dc.subjectFeature extractionen_US
dc.subjectTakagi-Sugeno-Kang (TSK) fuzzy logic system (FLS)en_US
dc.subjectTransfer learningen_US
dc.titleTakagi-Sugeno-Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signalsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1079en_US
dc.identifier.epage1094en_US
dc.identifier.volume24en_US
dc.identifier.issue5en_US
dc.identifier.doi10.1109/TFUZZ.2015.2501438en_US
dcterms.abstractThe intelligent recognition of electroencephalogram (EEG) signals has become an important approach to the detection of epilepsy. Among existing intelligent identification methods, fuzzy logic systems (FLSs) have shown a distinctive advantage in identifying epileptic EEG signals because of their strong learning abilities and interpretability. Like many conventional intelligent methods for recognizing EEG signals, in the training of FLS, it is assumed that the training dataset and test dataset are drawn from data that are identically distributed. However, this assumption is not necessarily valid in practice as it is not uncommon for the two datasets to have different distributions. To overcome this problem, a strategy is presented in this paper to construct a Takagi-Sugeno-Kang (TSK) FLS based on transductive transfer learning for identifying epileptic EEG signals. Two novel objective functions, achieved by integrating the transductive transfer learning mechanism, are proposed for the training of the TSK FLS. As regression and binary classification are two common approaches to multiclass classification, the TSK transfer learning FLS algorithms for regression and binary classification are developed, respectively, to construct the corresponding TSK FLS. Both algorithms are further used to perform a multiclass classification to recognize epileptic EEG signals. Their performance in the epileptic EEG datasets indicates promise in dealing with situations where the training and test datasets differ with regard to data distribution.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on fuzzy systems, Oct. 2016, v. 24, no. 5, p. 1079-1094en_US
dcterms.isPartOfIEEE transactions on fuzzy systemsen_US
dcterms.issued2016-10-
dc.identifier.isiWOS:000386076600007-
dc.identifier.eissn1941-0034en_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0597-n15-
dc.identifier.SubFormID455-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU5134/12Een_US
dc.description.pubStatusPublisheden_US
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