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

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Dades bibliogràfiques
Publicat a:	Symmetry vol. 17, no. 9 (2025), p. 1556-1575
Autor principal:	Liu, Yan
Altres autors:	Tu Chuanliang, Liu, Yong, Chen, Yu, Wen Chenggan, Yin Banghui
Publicat:	MDPI AG
Matèries:	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
Accés en línia:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripció
Resum:	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
Font:	Engineering Database