Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata
Joint Authors
Zhao, Xiangfu
Lamperti, Gianfranco
Ouyang, Dantong
Tong, Xiangrong
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-18
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
In the last several decades, the model-based diagnosis of discrete-event systems (DESs) has increasingly become an active research topic in both control engineering and artificial intelligence.
However, in contrast with the widely applied minimal diagnosis of static systems, in most approaches to the diagnosis of DESs, all possible candidate diagnoses are computed, including nonminimal candidates, which may cause intractable complexity when the number of nonminimal diagnoses is very large.
According to the principle of parsimony and the principle of joint-probability distribution, generally, the minimal diagnosis of DESs is preferable to a nonminimal diagnosis.
To generate more likely diagnoses, the notion of the minimal diagnosis of DESs is presented, which is supported by a minimal diagnoser for the generation of minimal diagnoses.
Moreover, to either strongly or weakly decide whether a minimal set of faulty events has definitely occurred or not, two notions of minimal diagnosability are proposed.
Necessary and sufficient conditions for determining the minimal diagnosability of DESs are proven.
The relationships between the two types of minimal diagnosability and the classical diagnosability are analysed in depth.
American Psychological Association (APA)
Zhao, Xiangfu& Lamperti, Gianfranco& Ouyang, Dantong& Tong, Xiangrong. 2020. Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata. Complexity،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1141878
Modern Language Association (MLA)
Zhao, Xiangfu…[et al.]. Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata. Complexity No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1141878
American Medical Association (AMA)
Zhao, Xiangfu& Lamperti, Gianfranco& Ouyang, Dantong& Tong, Xiangrong. Minimal Diagnosis and Diagnosability of Discrete-Event Systems Modeled by Automata. Complexity. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1141878
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141878