Adaptive IIR Filter Based on Penalized Spline

The purpose of this study is to develop a technique for real-time implementation of spline adaptive filters (SAFs). The P-SAF proposed in this paper is based on a recurrent penalized (P) spline and, by analogy with the classical SAF, consists of a linear dynamic component and nonlinear static component. To adapt the P-SAF, computational circuits with different topologies are developed. The proposed approach makes it possible to adapt knots and evaluate spline coefficients simultaneously. This improves the efficiency of the P‑SAF as compared to the classical SAF, as well as reduces the computational costs. The efficiency of the P‑SAF in terms of MSE[dB] on model and real-time series is equal to or higher than that of the classical SAF.

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Publication Details

Journal
Programming and Computer Software
Published
2024-11-19
DOI
https://doi.org/10.1134/s0361768824700300
Citations
3
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
1.12
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article

Adaptive IIR Filter Based on Penalized Spline

Elena Kochegurova, Yu. A. Martynova
3 citations
Programming and Computer Software
Advanced Adaptive Filtering Techniques
1.12
article

Adaptive IIR Filter Based on Penalized Spline

Elena Kochegurova, Yu. A. Martynova
article en
3 citations

Abstract

The purpose of this study is to develop a technique for real-time implementation of spline adaptive filters (SAFs). The P-SAF proposed in this paper is based on a recurrent penalized (P) spline and, by analogy with the classical SAF, consists of a linear dynamic component and nonlinear static component. To adapt the P-SAF, computational circuits with different topologies are developed. The proposed approach makes it possible to adapt knots and evaluate spline coefficients simultaneously. This improves the efficiency of the P‑SAF as compared to the classical SAF, as well as reduces the computational costs. The efficiency of the P‑SAF in terms of MSE[dB] on model and real-time series is equal to or higher than that of the classical SAF.

Programming and Computer SoftwareVol. 50(6)
National Research Tomsk State University (RU), Tomsk Polytechnic University (RU)
Affordable and clean energy
Openalex Percentile: Top 24%
Advanced Adaptive Filtering Techniques
1.12
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