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

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
Main Authors: Ionuț-Dorinel Fîciu, Cristian-Lucian Stanciu, Cristian Anghel, Camelia Elisei-Iliescu
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Applied Sciences
Subjects:
adaptive filters
dichotomous coordinate descent (DCD)
recursive least-squares (RLS)
system identification
tensor decomposition
Online Access:https://www.mdpi.com/2076-3417/11/18/8656
Description
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

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