Predictability of fuzzy discrete event systems

Benmessahel, Bilal; Touahria, Mohamed; Nouioua, Farid

doi:10.1007/s10626-017-0256-7

Cited by 22 publications

(2 citation statements)

References 36 publications

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“…In [5][6][7], the problem of predictability is studied in the context of stochastic DESs. Benmessahel et al [8] investigated the problem of predictability in fuzzy DESs. Yin and Li [9] studied the problem of reliable decentralized fault predictability.…”

Section: Introductionmentioning

confidence: 99%

Robust predictability in discrete event systems under sensor attacks

Zhang

2023

Front. Phys.

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The problem of robust predictability against sensor attacks is investigated. The objective of a diagnoser is to predict the occurrence of a critical event of a discrete event system (DES) under partial observation. An attacker may rewrite the diagnoser observation by inserting fake events or erasing real events. Two novel structures, namely, real diagnoser and the fake diagnoser, are constructed based on the diagnoser of the system. We compute the hybrid diagnoser as the parallel composition of the real diagnoser and the fake diagnoser. The hybrid diagnoser can be used to verify if a critical event of the system is robustly predictable when an attacker tampers with the diagnoser observation.

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Section: Introductionmentioning

confidence: 99%

Robust predictability in discrete event systems under sensor attacks

Zhang

2023

Front. Phys.

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“…Therefore, in the early 1980s, the Polish school proposed the interval analysis method as a tool to be used together with the fuzzy set method [18]. Since the combination of fuzzy set method [21] and interval analysis had a better effect, the concept of interval value fuzzy set was proposed and used for fuzzy reasoning [2].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Pushdown Automaton on Prediction of Quality Control for Spinning Yarn

Wang

Chen

et al. 2021

ITC

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In order to perform better recognition, tracking and control for fuzzy and uncertain thing, this paper will design a suitable fuzzy pushdown automaton (FPDA) control method to solve the problem. Firstly, the control design structure of FPDA and the decision reasoning rules in control are given. Secondly, the application of FPDA in prediction of quality control for spinning yarn is discussed in the practical problem. Finally, the comparison of FPDA and other control methods on the target control is given. The simulation results show that the control speed and the average precision of designed FPDA are faster by12ms and higher by 4.98% than that of traditional method, which its control precision is 96.87%.

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Notes on Supervisory Control of Fuzzy Discrete Event Systems

Lin

Qiu

2020

Intelligent Computing Methodologies

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Predictability of fuzzy discrete event systems

Cited by 22 publications

References 36 publications

Robust predictability in discrete event systems under sensor attacks

Robust predictability in discrete event systems under sensor attacks

Application of Fuzzy Pushdown Automaton on Prediction of Quality Control for Spinning Yarn

Notes on Supervisory Control of Fuzzy Discrete Event Systems

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