布尔控制网络的随机平均共识滤波与分布式状态估计
Stochastic Average Consensus Filtering and Distributed State Estimation for Boolean Control Networks
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中文总结 AI 辅助
针对布尔控制网络集中式估计的缺陷，本文结合概率测度变换等方法提出分布式随机平均共识滤波器，证明其收敛性，实现基于局部通信的全局状态估计。
中文摘要 AI 辅助
本文研究布尔控制网络（BCNs）的分布式多传感器融合状态估计与共识滤波问题。现有针对随机BCNs的集中式多传感器估计方案存在通信成本高、单点故障的缺陷，且连续状态共识算法难以扩展到离散逻辑系统。本文结合概率测度变换、半张量积与随机近似方法，提出分布式随机平均共识滤波器，利用鞅收敛定理与扰动随机李雅普诺夫函数证明算法的几乎必然收敛性。该框架通过局部通信实现全局状态估计，规避了集中式架构的缺陷。
英文摘要
This paper addresses the distributed multi-sensor fusion state estimation and consensus filtering for Boolean control networks (BCNs). Existing centralized multi-sensor estimation schemes for stochastic BCNs have limitations of high communication costs and single-point failures, and continuous-state consensus algorithms are difficult to extend to discrete logical systems. By integrating probability measure transformation, semi-tensor product and stochastic approximation, a distributed stochastic average consensus filter is proposed. Moreover, the almost sure convergence of the algorithm is proved by martingale convergence theorem and perturbed stochastic Lyapunov functions. The proposed framework realizes global state estimation via local communication, avoiding the defects of centralized architectures.
