Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

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 syst...

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Veröffentlicht in:	IET signal processing Jg. 14; H. 7; S. 455 - 466
Hauptverfasser:	Pan, Jian, Ma, Hao, Zhang, Xiao, Liu, Qinyao, Ding, Feng, Chang, Yufang, Sheng, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.09.2020
Schlagworte:	autoregressive moving average noise autoregressive moving average processes coloured noises coupled relationship coupling identification concept gradient methods gradient search hierarchical identification principle identification identification model input–output data least squares approximations multivariable control systems multivariable output‐error‐like system parameter estimation parameter estimation algorithm projection algorithm recursive coupled projection algorithms Research Article state‐space methods stochastic gradient algorithm stochastic processes least squares approximations coupled relationship coupling identification concept autoregressive moving average processes coloured noises stochastic gradient algorithm parameter estimation algorithm projection algorithm input–output data identification autoregressive moving average noise multivariable control systems gradient search identification model hierarchical identification principle parameter estimation multivariable output-error-like system stochastic processes gradient methods recursive coupled projection algorithms state-space methods
ISSN:	1751-9675, 1751-9683, 1751-9683
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Zusammenfassung:	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.
ISSN:	1751-9675 1751-9683 1751-9683
DOI:	10.1049/iet-spr.2019.0481