Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7719
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dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLam W.en_US
dc.date.accessioned2023-03-31T04:30:37Z-
dc.date.available2023-03-31T04:30:37Z-
dc.date.issued1989-
dc.identifier.citationJournal of Automated Reasoning, 1989, vol. 5 (2), pp. 207 - 233en_US
dc.identifier.issn15730670-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7719-
dc.description.abstractThis paper presents a comprehensive expert system shell which can deal with both exact and inexact reasoning. A prototype of this proposed shell, code named as SYSTEM Z-IIe, has been implemented successfully. It is a rule-based system which employs fuzzy logic and numbers for its reasoning. Two basic inexact concepts, fuzziness and uncertainty, are both used and distinct from each other clearly in the system. Moreover, these two concepts have been built into two levels for inexact reasoning, i.e. the level of the rules and facts, and the level of the values of the objects of these rules and facts. Other features of Z-IIe include multiple fuzzy propositions in rules and dual fact input mechanisms. It also allows any combinations of fuzzy and normal terms and uncertainties. Fuzzy numeric comparison logic control is also available for the rules and facts. Its natural language interface which uses English with restricted syntax improves the efficiency of knowledge engineering. Z-IIe is also coupled to a Database Management System for supplying facts from existing databases if appropriate. All these features can be combined to build very powerful expert systems and are illustrated by an example. © 1989 Kluwer Academic Publishers.en_US
dc.language.isoenen_US
dc.publisherKluwer Academic Publishersen_US
dc.relation.ispartofJournal of Automated Reasoningen_US
dc.titleA fuzzy expert system shell using both exact and inexact reasoningen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1007/BF00243003-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
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