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dc.contributor.authorLin, CTen_US
dc.date.accessioned2019-04-02T05:58:29Z-
dc.date.available2019-04-02T05:58:29Z-
dc.date.issued1996-10-01en_US
dc.identifier.issn0020-7721en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00207729608929297en_US
dc.identifier.urihttp://hdl.handle.net/11536/149347-
dc.description.abstractThe paper extends Kosko's fuzzy measure of subsethood (Kosko 1992) to a measure of mutual subsethood or fuzzy equivalence. Gaussian or bell-shaped fuzzy sets then simplify the new measure and allow supervised learning to learn and tune the fuzzy rules. The gaussian sets act as nodes in neural fuzzy control networks and give a simple closed form for the measure of mutual subsethood. The new adaptive subsethood controller (ASC) system uses the network structure to store, learn, and tune fuzzy rules. Simulations show how the ASC system can control a model car, balance an inverted wedge, and control the ball and beam system.en_US
dc.language.isoen_USen_US
dc.titleAdaptive subsethood for neural fuzzy controlen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00207729608929297en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF SYSTEMS SCIENCEen_US
dc.citation.volume27en_US
dc.citation.spage937en_US
dc.citation.epage955en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:A1996VQ25400005en_US
dc.citation.woscount2en_US
Appears in Collections:Articles