Open AccessData filtering‐based recursive identification for an exponential autoregressive moving average model by using the multi‐innovation theory
Author(s) -
Xu Huan,
Ma Fengying,
Ding Feng,
Xu Ling,
Alsaedi Ahmed,
Hayat Tasawar
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2020.0673
Subject(s) - autoregressive model , identification (biology) , noise (video) , autoregressive–moving average model , filter (signal processing) , computer science , moving average , exponential function , system identification , estimation theory , algorithm , mathematics , control theory (sociology) , mathematical optimization , artificial intelligence , data modeling , statistics , image (mathematics) , mathematical analysis , botany , control (management) , database , computer vision , biology
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.