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dc.contributor.authorHsiao, WHen_US
dc.contributor.authorChen, SMen_US
dc.contributor.authorLee, CHen_US
dc.date.accessioned2014-12-08T15:01:07Z-
dc.date.available2014-12-08T15:01:07Z-
dc.date.issued1998-01-01en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://hdl.handle.net/11536/41-
dc.description.abstractIn [7], Yan et al. analyzed Koczy and Hirota's linear interpolative reasoning method presented in [2,3] and found that the reasoning consequences by their method sometimes become abnormal fuzzy sets. Thus, they pointed out that a new interpolative reasoning method will be needed which can guarantee that the interpolated conclusion will also be triangular-type for a triangular-type observation. In this paper, we extend the works of [2,3,7] to present a new interpolative reasoning method to deal with fuzzy reasoning in sparse rule-based systems. The proposed method can overcome the drawback of Koczy and Hirota's method described in [7]. It can guarantee that the statement "If fuzzy rules A(1) double right arrow B-1, A(2) double right arrow B-2, and the observation A* are defined by triangular membership functions, the interpolated conclusion B* will also be triangular-type" holds. (C) 1998 Elsevier Science B.V.en_US
dc.language.isoen_USen_US
dc.subjectlinear interpolative reasoningen_US
dc.subjectfuzzy approximate reasoningen_US
dc.subjectsparse rule-based systemsen_US
dc.titleA new interpolative reasoning method in sparse rule-based systemsen_US
dc.typeArticleen_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume93en_US
dc.citation.issue1en_US
dc.citation.spage17en_US
dc.citation.epage22en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
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