Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7724| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
| dc.contributor.author | Lam W. | en_US |
| dc.date.accessioned | 2023-03-31T04:50:32Z | - |
| dc.date.available | 2023-03-31T04:50:32Z | - |
| dc.date.issued | 1988 | - |
| dc.identifier.citation | Computer, 1988, vol. 21 (9), pp. 43 - 56. | en_US |
| dc.identifier.issn | 0018-9162 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11861/7724 | - |
| dc.description.abstract | The authors present a comprehensive expert-system building tool, called System Z-II, that can deal with exact, fuzzy (or inexact), and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of an expert system. This fully implemented tool has been used to build several expert systems in the fields of student curriculum advisement, medical diagnosis, psychoanalysis, and risk analysis. System Z-II is a rule-based system that uses fuzzy logic and fuzzy numbers for its inexact reasoning. It uses two basic inexact concepts, fuzziness and uncertainty, which are distinct from each other in the system. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Computer | en_US |
| dc.title | Fuzzy concepts in expert systems | en_US |
| dc.type | Peer Reviewed Journal Article | en_US |
| dc.identifier.doi | 10.1109/2.14346 | - |
| item.fulltext | No Fulltext | - |
| crisitem.author.dept | Department of Applied Data Science | - |
| Appears in Collections: | Applied Data Science - Publication | |
SCOPUSTM
Citations
109
checked on Nov 17, 2024
Page view(s)
38
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.