A Framework of Fuzzy Diagnosis

Wang, Huaiqing, Zhang, Mingyi, Xu, D. and Zhang, Dan (2004) A Framework of Fuzzy Diagnosis. IEEE Transactions on Knowledge and Data Engineering, 16 12: 1571-1582. doi:10.1109/TKDE.2004.80

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
IEEETKDE-2004_DXU-1.pdf IEEETKDE-2004_DXU-1.pdf application/pdf 486.02KB 143

Author Wang, Huaiqing
Zhang, Mingyi
Xu, D.
Zhang, Dan
Title A Framework of Fuzzy Diagnosis
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2004
Sub-type Article (original research)
DOI 10.1109/TKDE.2004.80
Open Access Status File (Author Post-print)
Volume 16
Issue 12
Start page 1571
End page 1582
Total pages 12
Editor P. S. Yu
Place of publication Los Angeles, CA, USA
Publisher IEEE Computer Society
Collection year 2004
Language eng
Subject C1
700101 Application packages
0804 Data Format
Abstract Fault diagnosis has become an important component in intelligent systems, such as intelligent control systems and intelligent eLearning systems. Reiter's diagnosis theory, described by first-order sentences, has been attracting much attention in this field. However, descriptions and observations of most real-world situations are related to fuzziness because of the incompleteness and the uncertainty of knowledge, e. g., the fault diagnosis of student behaviors in the eLearning processes. In this paper, an extension of Reiter's consistency-based diagnosis methodology, Fuzzy Diagnosis, has been proposed, which is able to deal with incomplete or fuzzy knowledge. A number of important properties of the Fuzzy diagnoses schemes have also been established. The computing of fuzzy diagnoses is mapped to solving a system of inequalities. Some special cases, abstracted from real-world situations, have been discussed. In particular, the fuzzy diagnosis problem, in which fuzzy observations are represented by clause-style fuzzy theories, has been presented and its solving method has also been given. A student fault diagnostic problem abstracted from a simplified real-world eLearning case is described to demonstrate the application of our diagnostic framework.
Keyword Computer Science, Artificial Intelligence
Computer Science, Information Systems
Engineering, Electrical & Electronic
Knowledge Representation
Fuzzy Diagnosis
Fault Diagnosis
Uncertainty Reasoning
Fuzzy Truth Function Logic
Clause-style Fuzzy Theories
Fault-diagnosis
Logic
Abduction
Model
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
2005 Higher Education Research Data Collection
UQ Business School Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 15 Aug 2007, 03:18:28 EST