Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7705
DC FieldValueLanguage
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorSo Y.T.en_US
dc.date.accessioned2023-03-30T06:49:12Z-
dc.date.available2023-03-30T06:49:12Z-
dc.date.issued1993-
dc.identifier.citationInternational Journal of Approximate Reasoning, 1993, vol. 9 (3), pp. 263 - 282en_US
dc.identifier.issn0888613X-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7705-
dc.description.abstractInconsistency frequently exists in a rule-based expert system. Detecting the existence of inconsistency in a fuzzy rule-based environment is difficult and may be different from that of traditional rule-based systems. An affinity measure, which is based on the similarity measure, is introduced to determine the likeness of two fuzzy terms. By using the affinity measure, the techniques for consistency checking in a non-fuzzy environment can be easily applied to a fuzzy environment. A consistency checker (CCFE) is implemented to detect possible inconsistency in a mixed fuzzy and non-fuzzy environment. © 1993.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Approximate Reasoningen_US
dc.titleConsistency checking for fuzzy expert systemsen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1016/0888-613X(93)90013-4-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
Show simple item record

SCOPUSTM   
Citations

9
checked on Nov 17, 2024

Page view(s)

27
Last Week
1
Last month
checked on Nov 21, 2024

Google ScholarTM

Impact Indices

Altmetric

PlumX

Metrics


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