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dc.contributor.authorShang-Ming Zhou,
dc.contributor.authorGan, JQ
dc.date.accessioned2023-02-15T13:05:24Z
dc.date.available2023-02-15T13:05:24Z
dc.date.issued2009-08
dc.identifier.issn1041-4347
dc.identifier.issn1558-2191
dc.identifier.urihttp://hdl.handle.net/10026.1/20372
dc.description.abstract

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases. © 2006 IEEE.

dc.format.extent1191-1204
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectInterpretability
dc.subjectdistinguishability
dc.subjectknowledge extraction
dc.subjectlocal models
dc.subjectsubmodels
dc.subjectTakagi-Sugeno fuzzy models
dc.subjectregularization
dc.subjectfuzziness
dc.titleExtracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000267122600008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue8
plymouth.volume21
plymouth.publication-statusPublished
plymouth.journalIEEE Transactions on Knowledge and Data Engineering
dc.identifier.doi10.1109/tkde.2008.208
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
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dc.identifier.eissn1558-2191
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1109/tkde.2008.208
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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