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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Proceedings - International Conference on Parallel and Distributed Systems s. 2740 - 2741
Hlavní autoři:	Li, Wenyue, Zhao, Ji, Li, Qiang, Tang, Lingli, Zhang, Hongbin
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 17.12.2023
Témata:	Adaptive filters Adaptive systems Constrained adaptive filtering Filtering Filtering algorithms generalized correntropy Large scale integration non-Gaussian noise Simulation System identification
ISSN:	2690-5965
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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