Open AccessIterative parameter identification for pseudo‐linear systems with ARMA noise using the filtering technique
Author(s) -
Ding Feng,
Xu Ling,
Alsaadi Fuad E.,
Hayat Tasawar
Publication year - 2018
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.2017.0821
Subject(s) - autoregressive–moving average model , autoregressive model , algorithm , noise (video) , identification (biology) , system identification , convergence (economics) , estimation theory , computer science , moving average , mathematics , process (computing) , iterative method , control theory (sociology) , artificial intelligence , data modeling , statistics , botany , control (management) , biology , image (mathematics) , database , economics , economic growth , operating system
This study considers the parameter identification problem of a pseudo‐linear autoregressive moving average system (i.e. linear‐in‐parameter autoregressive output‐error ARMA systems), whose disturbance is an ARMA process. By means of the filtering technique, a filtering‐based gradient iterative (F‐GI) algorithm and a filtering‐based least squares iterative (LSI) algorithms are presented for enhancing the estimation accuracy. Furthermore, a filtering‐based decomposition LSI algorithm is derived for improving the computational efficiency. The key is to use the hierarchical identification principle, to apply the data filtering technique for identification, and to replace the unknown terms in the information vectors with their estimates. Compared with the F‐GI algorithm, the filtering‐based LSI algorithm and the filtering‐based decomposition LSI algorithm have faster convergence rates. The simulation results indicate that the proposed algorithms are effective.