Auxiliary model based identification methods.Part F: Performance Analysis

Performance analysis of identification methods is the important and difficult projects in the area of system identification. Once one new identification method is born, its convergence analysis appears. The auxiliary model identification is a branch of system identification and has become a large fa...

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Bibliographic Details
Published in:Nanjing Xinxi Gongcheng Daxue Xuebao Vol. 8; no. 6; p. 481
Main Author: Feng, Ding
Format: Journal Article
Language:Chinese
English
Published: Nanjing Nanjing University of Information Science & Technology 01.12.2016
Subjects:
Algorithms
Convergence
Identification methods
Least squares method
Mathematical models
Stochasticity
System identification
Systems analysis
ISSN:1674-7070
Online Access:Get full text
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Summary:Performance analysis of identification methods is the important and difficult projects in the area of system identification. Once one new identification method is born, its convergence analysis appears. The auxiliary model identification is a branch of system identification and has become a large family of identification methods, their convergence brings many projects. This paper studies the consistent convergence of the auxiliary model (AM) based stochastic gradient ( SG ) algorithm, the AM recursive least squares (RLS) algorithm, the AM multiinnovation SG algorithm, the interval-varying AM SG algorithm and the interval-varying AM RLS algorithm for out-put-error systems, and analyzes approximately the convergence of the AM recursive generalized extended least squares algorithm for Box-Jenkins systems.
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ISSN:1674-7070
DOI:10.13878/j.cnki.jnuist.2016.06.001