Decentralized identification for errors-in-variables systems based on a consensus algorithm

M. Stanković, S. Stankovic, D. Stipanović
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引用次数: 2

Abstract

In this paper a new consensus based algorithm for decentralized recursive estimation of parameters in linear discrete-time stochastic errors-in-variables MIMO systems is proposed. One starts from a multi-agent setting, in which an agent has access only to a subset of noisy input-output variables. The proposed algorithm consists of two stages. The first stage is based on a combination of local stochastic approximation algorithms for estimating input-output covariance functions based on locally available measurements and a dynamic first order consensus scheme. At the second stage each agent utilizes a stochastic approximation algorithm with expanding truncations for generating all system parameter estimates on the basis of current estimates of the matrices in the modified Yule-Walker equations obtained at the first stage. In the given convergence analysis it is proved that the estimates of the covariance functions and the overall parameter estimates converge almost surely to their true values under appropriate assumptions concerning system properties and the multi-agent network topology.
基于共识算法的变量误差系统去中心化识别
本文提出了一种新的基于一致性的线性离散随机变量误差多输入多输出系统参数分散递推估计算法。一种是从多代理设置开始的,其中代理只能访问噪声输入输出变量的子集。该算法分为两个阶段。第一阶段是基于局部可用测量值估计输入输出协方差函数的局部随机逼近算法和动态一阶一致性方案的结合。在第二阶段,每个智能体利用扩展截断的随机逼近算法,根据第一阶段得到的修正Yule-Walker方程中矩阵的当前估计,生成所有系统参数估计。在给定的收敛性分析中,证明了在适当的系统性质和多智能体网络拓扑假设下,协方差函数估计和总体参数估计几乎肯定收敛于它们的真值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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