Auxiliary Model Hierarchical Iterative Parameter Estimation for Autoregressive Output‐Error Models

International Journal of Adaptive Control and Signal Processing 2026

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Authors: Feng Ding, Hao Fang, Ling Xu
DOI: 10.1002/acs.70081
Cited by: 8
References: 124

Abstract

ABSTRACT By means of the auxiliary model identification idea and the multi‐innovation identification theory, this paper investigates iterative parameter identification methods for autoregressive output‐error models, and proposed an auxiliary model (hierarchical) gradient‐based iterative (GI) algorithm, an auxiliary model (hierarchical) least‐squares‐based iterative (LSI) algorithm, an auxiliary model (hierarchical) multi‐innovation GI algorithm, an auxiliary model (hierarchical) multi‐innovation LSI algorithm by using the hierarchical identification principle. The proposed hierarchical iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises.

Fields of Study & Subjects

Control Systems and Identification Structural Health Monitoring Techniques Advanced Adaptive Filtering Techniques

Publication details

Journal / source
International Journal of Adaptive Control and Signal Processing
Publisher
Wiley
Volume
40
Issue
7
Pages
1486–1513
Publication date
18 March 2026
Language
EN