子空间估计的渐近方差

A. Chiuso, G. Picci
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引用次数: 8

摘要

本文给出了一种常用的子空间辨识方法得到的状态空间模型的估计参数(A, B, C, D)的渐近协方差的新表达式。这些表达式与迄今为止已发表的渐近协方差公式相似,但更简单,涉及条件协方差矩阵/spl Sigma/(xx|u/sup +/), /spl Sigma/(u/sup +/u/sup +/|x)的逆,从而提供了估计问题可能的病态与估计的渐近方差的直接联系。对子空间辨识的病态条件进行了研究。该公式可应用于N4SID、MOESP、CVA等子空间方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Asymptotic variances of subspace estimates
We provide new expressions for the asymptotic covariance of the estimated parameters (A, B, C, D) of a state space model obtained by some popular subspace identification method. The expressions, similar but simpler than the asymptotic covariance formulas which have so far been published in the literature, involve the inverses of the conditional covariance matrices /spl Sigma/(xx|u/sup +/), /spl Sigma/(u/sup +/u/sup +/|x) thus providing a direct link of possible ill-conditioning of the estimation problem with the asymptotic variance of the estimates. A study of ill-conditioning of subspace identification has been presented. The formulas can be applied to several subspace methods including N4SID, MOESP, CVA, etc.
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