Constraint-Forcing Recursive Generalized Maximum Correntropy Algorithm with Forgetting Factor for Adaptive Filtering

In this paper, jointly with the exponential weighted generalized maximum correntropy (GMC) criterion and the linear constraint framework, we derive a recursive constrained adaptive filtering algorithm named recursive constrained GMC with forgetting factor (FF-RCGMC). In addition, due to a lack of co...

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Bibliographic Details
Published in:Proceedings - International Conference on Parallel and Distributed Systems pp. 2740 - 2741
Main Authors: Li, Wenyue, Zhao, Ji, Li, Qiang, Tang, Lingli, Zhang, Hongbin
Format: Conference Proceeding
Language:English
Published: IEEE 17.12.2023
Subjects:
Adaptive filters
Adaptive systems
Constrained adaptive filtering
Filtering
Filtering algorithms
generalized correntropy
Large scale integration
non-Gaussian noise
Simulation
System identification
ISSN:2690-5965
Online Access:Get full text
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Summary:In this paper, jointly with the exponential weighted generalized maximum correntropy (GMC) criterion and the linear constraint framework, we derive a recursive constrained adaptive filtering algorithm named recursive constrained GMC with forgetting factor (FF-RCGMC). In addition, due to a lack of constraint information during the learning process, FF-RCGMC will diverge or even fail to work after some iterations. Therefore, we propose a more stable version by introducing a constraint-forcing strategy into FF-RCGMC and call this robust type as constraint-forcing FF-RCGMC (CFFF-RCGMC). Some simulation results in system identification under non-Gaussian noisy environments validate the effectiveness of CFFF-RCGMC.
ISSN:2690-5965
DOI:10.1109/ICPADS60453.2023.00366