ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Zero-Attracting Kernel Maximum Versoria Criterion Algorithm for Nonlinear Sparse System Identification

Jain, S and Majhi, S (2022) Zero-Attracting Kernel Maximum Versoria Criterion Algorithm for Nonlinear Sparse System Identification. In: IEEE Signal Processing Letters, 29 . pp. 1546-1550.

[img] PDF
IEEE_sig_pro_let_29_1546-1550_2022.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: https://doi.org/10.1109/LSP.2022.3182139


Sparsity-induced kernel adaptive filters have emerged as a promising candidate for a nonlinear sparse system identification (SSI) problem. The existing zero-attracting kernel least mean square (ZA-KLMS) algorithm relies on minimum mean square error criterion, which considers only second order statistics of error, thereby resulting in suboptimal performance in the presence of non-Gaussian/impulsive distortions. In this letter, we propose a novel random Fourier features (RFF) based ZA kernel maximum Versoria criterion (ZA-KMVC) algorithm, and their variants, which are robust for nonlinear SSI in the presence of non-Gaussian distortions over both stationary and time-varying environments. Furthermore, the mean-square convergence analysis of the proposed RFF-ZA-KMVC algorithm is performed. It has been observed from the simulation results that the proposed algorithm delivers better convergence performance as compared to the existing state-of-art approaches. © 2022 IEEE.

Item Type: Journal Article
Publication: IEEE Signal Processing Letters
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords: Adaptive filtering; Adaptive filters; Bandpass filters; Error statistics; Gaussian distribution; Gaussian noise (electronic); Mean square error; Religious buildings, Convergence; Cost-function; Fourier features; Kernel; Kernel least mean squares; KLMS; Maximum versorium criteria; Non-Gaussian; Prediction algorithms; Random fourier feature; Reproducing Kernel Hilbert spaces; Signal processing algorithms; Sparsity-aware; Steady state; ZA kernel maximum versorium criteria; Zero-attracting; Zero-attracting kernel least mean square, Cost functions
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 21 Sep 2022 09:51
Last Modified: 21 Sep 2022 09:51
URI: https://eprints.iisc.ac.in/id/eprint/76776

Actions (login required)

View Item View Item