Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classic...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 18; p. 8656
Main Authors Fîciu, Ionuț-Dorinel, Stanciu, Cristian-Lucian, Anghel, Cristian, Elisei-Iliescu, Camelia
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
Subjects
Adaptive algorithms
adaptive filters
Adaptive systems
Algorithms
Complexity
Decomposition
dichotomous coordinate descent (DCD)
Identification
Methods
Numerical stability
recursive least-squares (RLS)
System identification
tensor decomposition
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Summary:Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11188656