Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81702
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Health Technology and Informatics-
dc.creatorZhang, Yen_US
dc.creatorDong, JCen_US
dc.creatorZhu, JQen_US
dc.creatorWu, CYen_US
dc.date.accessioned2020-02-10T12:28:43Z-
dc.date.available2020-02-10T12:28:43Z-
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10397/81702-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0en_US
dc.rightsThe following publication Y. Zhang, J. Dong, J. Zhu and C. Wu, "Common and Special Knowledge-Driven TSK Fuzzy System and Its Modeling and Application for Epileptic EEG Signals Recognition," in IEEE Access, vol. 7, pp. 127600-127614, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2937657en_US
dc.subjectCommon knowledgeen_US
dc.subjectFLNNen_US
dc.subjectGMMen_US
dc.subjectLLMen_US
dc.subjectSpecial knowledgeen_US
dc.subjectTSK fuzzy systemsen_US
dc.titleCommon and special knowledge-driven TSK fuzzy system and its modeling and application for epileptic EEG signals recognitionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage127600en_US
dc.identifier.epage127614en_US
dc.identifier.volume7en_US
dc.identifier.doi10.1109/ACCESS.2019.2937657en_US
dcterms.abstractTakagi-Sugeno-Kang (TSK) fuzzy systems are well known for their good balances between approximation accuracy and interpretability. Among a wide variety of existing TSK fuzzy systems, most of them are driven by special knowledge since the learned parameters of each fuzzy rule are totally different. However, common knowledge is equally important and useful in practice and hence a TSK fuzzy system embedded with common knowledge should be more intuitive and interpretable when tackling with real-world problems. In this paper, we propose a common and special knowledge-driven TSK fuzzy system (CSK-TSK-FS), in which the parameters corresponding to each feature in then-parts of fuzzy rules always keep invariant and these parameters are viewed as common knowledge. As for its modeling, except the gradient descent techniques and other existing training algorithms, we can obtain a trained CSK-TSK-FS from a trained GMM or a trained FLNN because the proposed fuzzy system CSK-TSK-FS is mathematically equivalent to a special GMM and a FLNN. CSK-TSK-FS has three characteristics: (1) with the classical centroid defuzzification strategy, the involved common knowledge can be separated from fuzzy rules such that the interpretability of CSK-TSK-FS can be enhanced; (2) it can be trained quickly by the proposed LLM-based training algorithm; (3) the equivalence relationships among CSK-TSK-FS, GMM and FLNN allow them to share some commonality in training such that the proposed LLM-based training algorithm provides a novel fast training tool for training GMM and FLNN. Experimental results on UCI, KEEL and epileptic EEG datasets demonstrate the promising classification of CSK-TSK-FS.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2019, v. 7, p. 127600-127614en_US
dcterms.isPartOfIEEE accessen_US
dcterms.issued2019-
dc.identifier.isiWOS:000487231700076-
dc.identifier.scopus2-s2.0-85078032197-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Zhang_Knowledge-Driven_TSK_Fuzzy.pdf2.91 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

45
Citations as of Jun 26, 2022

Downloads

56
Citations as of Jun 26, 2022

SCOPUSTM   
Citations

13
Citations as of Jun 30, 2022

WEB OF SCIENCETM
Citations

13
Citations as of Jun 30, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.