期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 68, 期 3, 页码 1815-1822出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2022.3157078
关键词
Stochastic processes; Fault detection; Switches; Probability distribution; Observers; Manganese; Iterative methods; Markovian jump Boolean networks (MJBNs); optimal state estimation; reconstructibility; semi-tensor product (STP); state distribution
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.
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.
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