Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/72825
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dc.titleOn the learning of min-max fuzzy systems
dc.contributor.authorTan, Shaohua
dc.contributor.authorZhang, Li
dc.contributor.authorVandewalle, Joos
dc.date.accessioned2014-06-19T05:12:26Z
dc.date.available2014-06-19T05:12:26Z
dc.date.issued1997
dc.identifier.citationTan, Shaohua,Zhang, Li,Vandewalle, Joos (1997). On the learning of min-max fuzzy systems. IEEE International Conference on Fuzzy Systems 3 : 1581-1584. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72825
dc.description.abstractThis paper develops a methodology for learning fuzzy systems that contain min-max operations. It is shown that with the defuzzification properly defined, a min-max fuzzy system can have a transparent structure that is both universally approximating and easy for a learning scheme to be developed. A specific learning scheme based on multi-scale residue extraction is then presented.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleIEEE International Conference on Fuzzy Systems
dc.description.volume3
dc.description.page1581-1584
dc.description.codenPIFSF
dc.identifier.isiutNOT_IN_WOS
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