文献类型：期刊文献

英文题名：Multi-Sensor Fusion Boolean Bayesian Filtering for Stochastic Boolean Networks

作者：Li, Fangfei[1,2];Tang, Yang[2]

机构：[1]East China Univ Sci & Technol, Sch Math, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：34

期号：10

起止页码：7114

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20220411531663);WOS:【SCI-EXPANDED(收录号:WOS:000742697900001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62173142 and Grant 61773161, in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300, in part by the Sino-German Center for Research Promotion under Grant M-0066, and in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Estimation; Kalman filters; Bayes methods; State estimation; Sensor fusion; Stochastic processes; Genetics; Boolean Bayesian filtering; semi-tensor product (STP); state estimation; stochastic Boolean networks (SBNs)

摘要：Stochastic Boolean networks (SBNs) take process noise into account, so it is better to fit the actual situation and has a wider application background than Boolean networks (BNs). However, the presence of noise influences us to estimate the real state of the system. To minimize the inaccuracies caused by the presence of noise, an optimal state estimation problem is studied in this article. The multi-sensor fusion Boolean Bayesian filtering is proposed and a recursive algorithm is provided to calculate the prior and posterior belief of system state by fusing multi-sensor measurements based on the algebraic form of the SBN and Bayesian law. Then, the optimal state estimator is obtained, which minimizes the mean-square estimation error. Finally, a simulation example is carried out to demonstrate the performance of the proposed methodology. It has been shown through the simulation experiment that it increases the confidence level of the state estimation and improves the estimation performance using multi-sensor fusion compared with using single sensor.