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Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory
Author(s) -
Ma Ping,
Wang Lei
Publication year - 2021
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3302
Subject(s) - recursive least squares filter , multivariate statistics , least squares function approximation , mathematics , estimation theory , generalized least squares , algorithm , noise (video) , mathematical optimization , computer science , adaptive filter , statistics , image (mathematics) , artificial intelligence , estimator
Summary This article researches the filtering‐based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering‐based recursive generalized extended least squares algorithm is derived, in which the data filtering technique is used for transforming the original system into two subidentification systems and the least squares principle is used for estimating parameters of these two subsystems. Furthermore, in order to improve the parameter estimation accuracy, the multiinnovation theory is added for deducing a filtering‐based multiinnovation recursive generalized extended least squares algorithm. The numerical example confirms that these two proposed algorithms are effective.