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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Vydané v:	IET control theory & applications Ročník 14; číslo 17; s. 2526 - 2534
Hlavní autori:	Xu, Huan, Ma, Fengying, Ding, Feng, Xu, Ling, Alsaedi, Ahmed, Hayat, Tasawar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 26.11.2020
Predmet:	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
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Shrnutí:	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