Kernel Mean p-Power Loss-Enhanced Robust Hammerstein Adaptive Filter and Its Performance Analysis

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Detalles Bibliográficos
Publicado en:	Symmetry vol. 17, no. 9 (2025), p. 1556-1575
Autor Principal:	Liu, Yan
Outros autores:	Tu Chuanliang, Liu, Yong, Chen, Yu, Wen Chenggan, Yin Banghui
Publicado:	MDPI AG
Materias:	Mean square errors Random variables Normal distribution Signal processing Approximation Design Random noise Noise System identification Filter design (mathematics) Algorithms Nonlinear systems Adaptive filters Robustness
Acceso en liña:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumo:	Hammerstein adaptive filters (HAFs) are widely used for nonlinear system identification due to their structural simplicity and modeling effectiveness. However, their performance can degrade significantly in the presence of impulsive disturbance or other more complex non-Gaussian noise, which are common in real-world scenarios. To address this limitation, this paper proposes a robust HAF algorithm based on the kernel mean p-power error (KMPE) criterion. By extending the p-power loss into the kernel space, KMPE preserves its symmetry while providing enhanced robustness against non-Gaussian noise in adaptive filter design. In addition, random Fourier features are employed to flexibly and efficiently model the nonlinear component of the system. A theoretical analysis of steady-state excess mean square error is presented, and our simulation results validate the superior robustness and accuracy of the proposed method over the classical HAF and its robust variants.
ISSN:	2073-8994
DOI:	10.3390/sym17091556
Fonte:	Engineering Database