State Distribution of Markovian Jump Boolean Networks and Its Applications

This article investigates the state distribution of Markovian jump Boolean networks subject to stochastic disturbances based on the measured outputs. The considered disturbances are modeled as independent and identically distributed processes with known probability distributions. An iterative algori...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 68; no. 3; pp. 1815 - 1822
Main Authors Meng, Min, Xiao, Gaoxi
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Boolean functions
Conditional probability
Disturbances
Fault detection
Iterative algorithms
Iterative methods
Manganese
Markovian jump Boolean networks (MJBNs)
Networks
Observers
optimal state estimation
Probability distribution
reconstructibility
semi-tensor product (STP)
state distribution
State estimation
Stochastic processes
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Summary:This article investigates the state distribution of Markovian jump Boolean networks subject to stochastic disturbances based on the measured outputs. The considered disturbances are modeled as independent and identically distributed processes with known probability distributions. An iterative algorithm is proposed to compute conditional probability distributions of the current state and one-step predicted state based on the knowledge of the output measurements. The obtained conditional probability distributions can be applied to study the optimal state estimation, reconstructibility, and fault detection of Markovian jump Boolean networks.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2022.3157078