Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise

被引：2

作者：

Ivanov, Dmitriy ^{[1
,2
]}

Yakoub, Zaineb ^{[3
]}

机构：

[1] Samara Natl Res Univ, Dept Informat Syst Secur, Samara 443086, Russia

[2] Samara State Univ Transport, Dept Mechatron, Samara 443066, Russia

[3] Univ Gabes, Natl Engn Sch Gabes, Dept Elect Engn, Gabes 6029, Tunisia

来源：

MATHEMATICS | 2023年 / 11卷 / 03期

关键词：

autoregressive model; additive noise; Yule-Walker equations; bias-compensated least squares; Frisch scheme; total least squares; errors-in-variables; prediction error method; maximum likelihood; LEAST-SQUARES; PARAMETER-ESTIMATION; YULE-WALKER; AR PARAMETERS; LINEAR-SYSTEMS; SIGNALS; ALGORITHMS; COEFFICIENTS; CRITERION;

D O I：

10.3390/math11030607

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods.

引用

页数：21

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