Data filtering-based recursive identification for an exponential autoregressive moving average model by using the multi-innovation theory

This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into...

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Bibliographic Details
Published in:IET control theory & applications Vol. 14; no. 17; pp. 2526 - 2534
Main Authors: Xu, Huan, Ma, Fengying, Ding, Feng, Xu, Ling, Alsaedi, Ahmed, Hayat, Tasawar
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 26.11.2020
Subjects:
autoregressive processes
average noise
coloured noise model
data filtering‐based recursive identification
ExpARMA model
filtering theory
filtering‐based multiinnovation extended stochastic gradient algorithm
gradient methods
linear filter
multiinnovation identification theory
nonlinear exponential autoregressive model
recursive estimation
Research Article
nonlinear exponential autoregressive model
linear filter
filtering theory
average noise
recursive estimation
coloured noise model
autoregressive processes
data filtering-based recursive identification
filtering-based multiinnovation extended stochastic gradient algorithm
gradient methods
multiinnovation identification theory
ExpARMA model
ISSN:1751-8644, 1751-8652
Online Access:Get full text
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Summary:This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering-based extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, the multi-innovation identification theory is used to develop a filtering-based multi-innovation extended stochastic gradient algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering-based multi-innovation algorithm over the existing algorithms.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2020.0673