State Distribution of Markovian Jump Boolean Networks and Its Applications

• 成果类型: Article
• 署名作者: Meng, Min; Xiao, Gaoxi
• 署名单位: Tongji University; Tongji University; Nanyang Technological University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3157078
• 发表日期: 2023
• 页码: 1815-1822
• 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

摘要

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.