Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise

This study focuses on the recursive parameter estimation problems for the non-linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extended stochastic gradient (ESG) algorithm is derived. Considering the difficulty of de...

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Vydáno v:	IET control theory & applications Ročník 14; číslo 2; s. 262 - 270
Hlavní autoři:	Xu, Huan, Ding, Feng, Yang, Erfu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 29.01.2020
Témata:	appropriate innovation length autoregressive moving average processes average noise coloured noise ExpARMA model exponential autoregressive time series model extended stochastic gradient algorithm forgetting factor gradient methods gradient search least squares approximations MI‐ESG algorithm multiinnovation ESG algorithm multiinnovation identification theory nonlinear exponential autoregressive model optimal step‐size parameter estimation parameter estimation accuracy recursive estimation recursive parameter estimation problems recursive search‐based identification algorithms Research Article stochastic processes time series unknown parameters least squares approximations appropriate innovation length autoregressive moving average processes parameter estimation accuracy average noise time series recursive search-based identification algorithms recursive parameter estimation problems optimal step-size multiinnovation identification theory multiinnovation ESG algorithm nonlinear exponential autoregressive model MI-ESG algorithm gradient search recursive estimation forgetting factor coloured noise parameter estimation extended stochastic gradient algorithm stochastic processes gradient methods unknown parameters exponential autoregressive time series model ExpARMA model
ISSN:	1751-8644, 1751-8652
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