Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7708
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.contributor.author | Wong M.H. | en_US |
dc.date.accessioned | 2023-03-30T06:58:47Z | - |
dc.date.available | 2023-03-30T06:58:47Z | - |
dc.date.issued | 1992 | - |
dc.identifier.citation | International Journal of Intelligent Systems, 1992, vol. 7 (2), pp. 171 - 192 | en_US |
dc.identifier.issn | 08848173 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7708 | - |
dc.description.abstract | Fuzzy logic is one of the methods to model the vagueness and imprecision of human knowledge. Some rule‐based expert system shells have been successfully developed and have demonstrated the power of fuzzy logic in dealing with inexact reasoning and rule inferences. However, using rules for knowledge representation is not structured enough. In addition, knowledge cannot be easily represented in an abstracted (hierarchical) from. In this article the introduction of fuzzy concepts into object oriented knowledge representation (OOKR), which is a structured knowledge representation scheme, is presented. A framework for handling all the possible fuzzy concepts in OOKR at both the dynamic and static levels is proposed. In order to handle the inheritance mechanism and to model the relations among classes, instances, and attributes, some new fuzzy concepts and operations are introduced. These concepts and operations are developed from the semantic meaning rather than by an ad hoc approach. A prototype of the expert system shell. System FX‐I, has been successfully developed based on the above framework, showing the feasibility of handling inexact knowledge in a structural way. Copyright © 1992 Wiley Periodicals, Inc., A Wiley Company | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | International Journal of Intelligent Systems | en_US |
dc.title | Fuzzy concepts in an object oriented expert system shell | en_US |
dc.type | Peer Reviewed Journal Article | en_US |
dc.identifier.doi | 10.1002/int.4550070206 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
Appears in Collections: | Applied Data Science - Publication |
SCOPUSTM
Citations
18
checked on Nov 17, 2024
Page view(s)
34
Last Week
1
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.