Open AccessRecursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises
Author(s) -
Pan Jian,
Ma Hao,
Zhang Xiao,
Liu Qinyao,
Ding Feng,
Chang Yufang,
Sheng Jie
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2019.0481
Subject(s) - multivariable calculus , autoregressive model , projection (relational algebra) , algorithm , identification (biology) , noise (video) , system identification , autoregressive–moving average model , key (lock) , computer science , coupling (piping) , mathematics , estimation theory , control theory (sociology) , artificial intelligence , data modeling , statistics , control (management) , control engineering , mechanical engineering , botany , computer security , database , image (mathematics) , biology , engineering
By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output‐error‐like system with autoregressive moving average noise from input–output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.