Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

Fîciu, Ionuț-Dorinel; Stanciu, Cristian-Lucian; Elisei-Iliescu, Camelia; Anghel, Cristian

doi:10.3390/electronics11020237

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…A larger value of δ will tend to slow down (or even freeze) the filter updates, which is preferable in low SNR conditions. [11][12][13] The update equation of the regularized RLS can be rewritten as:…”

Section: Regularized Rls Algorithmmentioning

confidence: 99%

Low-complexity data-reuse RLS algorithm with increased robustness features

Fîciu

Stanciu²,

Elisei-Iliescu³

et al. 2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

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Recent advancements in the field of adaptive filters based on the least-squares minimization criterion propose a stable and efficient recursive least-squares (RLS) algorithm with attractive numerical properties and performance similar to the classical RLS methods. The combination between the RLS and the dichotomous coordinate descent (DCD) iterations (i.e., the RLS-DCD) offers an attractive option for practical applications with low requirements in terms of chip area. This paper employs a data-reuse methodology to further improve the tracking speed of the RLS-DCD algorithm. Furthermore, a regularization principle is used to develop its robustness features in low signal-to-noise working conditions.

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Section: Regularized Rls Algorithmmentioning

confidence: 99%

Low-complexity data-reuse RLS algorithm with increased robustness features

Fîciu

Stanciu²,

Elisei-Iliescu³

et al. 2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

Self Cite

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“…In [3], Fîciu et al develop a robust and computationally efficient tensor-based recursive least-squares (RLS) algorithm for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed based on tensor decomposition and modeling.…”

Section: Short Presentation Of the Papersmentioning

confidence: 99%

Efficient Algorithms and Architectures for DSP Applications

Chiper

Paleologu

2023

Electronics

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Identification of Multilinear Systems: A Brief Overview

Dogariu¹,

Paleologu²,

Benesty³

et al. 2022

Advances in Principal Component Analysis

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Nonlinear systems have been studied for a long time and have applications in numerous research fields. However, there is currently no global solution for nonlinear system identification, and different used approaches depend on the type of nonlinearity. An interesting class of nonlinear systems, with a wide range of popular applications, is represented by multilinear (or multidimensional) systems. These systems exhibit a particular property that may be exploited, namely that they can be regarded as linearly separable systems and can be modeled accordingly, using tensors. Examples of well-known applications of multilinear forms are multiple-input/single-output (MISO) systems and acoustic echo cancellers, used in multi-party voice communications, such as videoconferencing. Many important fields (e.g., big data, machine learning, and source separation) can benefit from the methods employed in multidimensional system identification. In this context, this chapter aims to briefly present the recent approaches in the identification of multilinear systems. Methods relying on tensor decomposition and modeling are used to address the large parameter space of such systems.

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Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

Cited by 3 publications

References 31 publications

Low-complexity data-reuse RLS algorithm with increased robustness features

Low-complexity data-reuse RLS algorithm with increased robustness features

Efficient Algorithms and Architectures for DSP Applications

Identification of Multilinear Systems: A Brief Overview

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