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Zero-Attracting Kernel Maximum Versoria Criterion Algorithm for Nonlinear Sparse System Identification
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DOI：10.1109/LSP.2022.3182139.png)
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Abstract
En 中文
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.
Keywords:
Signal processing algorithms
Kernel
Prediction algorithms
Cost function
Convergence
Adaptive filters
Steady-state
KLMS
maximum Versoria criterion
non-Gaussian
random Fourier features
reproducing kernel Hilbert space
sparsity-aware
ZA-KLMS
ZA-KMVC
zero-attracting
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
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Cited Papers
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