Bias-eliminating least-squares identification of errors-in-variables models with mutually correlated noises

被引：11

作者：

Diversi, Roberto ^{[1
]}

机构：

[1] Univ Bologna, Dept Elect Elect & Informat Engn Guglielmo Marcon, I-40136 Bologna, Italy

来源：

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2013年 / 27卷 / 10期

关键词：

system identification; errors-in-variables models; bias-eliminating least squares; mutually correlated white noises; FRISCH SCHEME; INPUT;

D O I：

10.1002/acs.2365

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a bias-eliminating least-squares (BELS) approach for identifying linear dynamic errors-in-variables (EIV) models whose input and output are corrupted by additive white noise. The method is based on an iterative procedure involving, at each step, the estimation of both the system parameters and the noise variances. The proposed identification algorithm differs from previous BELS algorithms in two aspects. First, the input and output noises are allowed to be mutually correlated, and second, the estimation of the noise covariances is obtained by exploiting the statistical properties of the equation error of the EIV model. Copyright (c) 2012 John Wiley & Sons, Ltd.

引用

页码：915 / 924

页数：10

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