Nonlinear Spline Adaptive Filtering Against Non-Gaussian Noise

In this paper, a generalized maximum Versoria criterion algorithm (GMVC) based on wiener spline adaptive filter, called SAF–GMVC, is proposed. The proposed algorithm is used for nonlinear system identification under non-Gaussian environment. To improve the convergence performance of the SAF–GMVC, th...

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Published inCircuits, systems, and signal processing Vol. 41; no. 1; pp. 579 - 596
Main Authors Guo, Wenyan, Zhi, Yongfeng
Format Journal Article
LanguageEnglish
Published New York Springer US 01.01.2022
Springer Nature B.V
Subjects
Adaptive filters
Algorithms
Circuits and Systems
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Nonlinear systems
Random noise
Signal,Image and Speech Processing
System identification
Robust filtering
Generalized maximum Versoria criterion
Spline adaptive filter
Nonlinear system identification
Non-Gaussian noise
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Summary:In this paper, a generalized maximum Versoria criterion algorithm (GMVC) based on wiener spline adaptive filter, called SAF–GMVC, is proposed. The proposed algorithm is used for nonlinear system identification under non-Gaussian environment. To improve the convergence performance of the SAF–GMVC, the momentum stochastic gradient descent (MSGD) is introduced. In order to further reduce the steady-state error, the variable step-size algorithm is introduced, called as SAF–GMVC–VMSGD. Simulation results demonstrate that SAF–GMVC–VMSGD achieves better filtering effective against non-Gaussian noise.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-021-01798-3