arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.14765eess.SYcs.SYmath.OC

分层参数估计用于分布式网络系统:一种动态共识方法

Hierarchical parameter estimation for distributed networked systems: a dynamic consensus approach

Ariana R. Mendez-Castillo, Rodrigo Aldana-Lopez, Antonio Ramirez-Trevino, Rosario Aragues, David Gomez-Gutierrez

AI总结:

本文提出一种两阶段分布式框架,用于全局估计网络系统中的常数参数,通过动态平均共识聚合代理测量,再利用局部估计器确定参数,设计合适共识增益以保证局部梯度估计器的指数收敛。

摘要:

本文介绍了一种新颖的两阶段分布式框架,用于全局估计网络系统中的常数参数,将共享信息与局部估计分离。第一阶段使用动态平均共识将代理的测量聚合为集中式数据的代理。利用这些代理,第二阶段实现局部估计器以确定参数。通过设计适当的共识增益,实现回归矩阵的持续激励,从而保证局部梯度估计器(GE)的指数收敛。该框架可扩展到切换网络拓扑、量化和将GE异构替换为动态回归扩展和混合(DREM)估计器,从而支持更宽松的激励要求。

英文摘要:

This work introduces a novel two-stage distributed framework to globally estimate constant parameters in a networked system, separating shared information from local estimation. The first stage uses dynamic average consensus to aggregate agents' measurements into surrogates of centralized data. Using these surrogates, the second stage implements a local estimator to determine the parameters. By designing an appropriate consensus gain, the persistence of excitation of the regressor matrix is achieved, and thus, exponential convergence of a local Gradient Estimator (GE) is guaranteed. The framework facilitates its extension to switched network topologies, quantization, and the heterogeneous substitution of the GE with a Dynamic Regressor Extension and Mixing (DREM) estimator, which supports relaxed excitation requirements.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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