Bias-compensated Sparse RLS Algorithms Over Distributed Networks

In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give...

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Veröffentlicht in:Chinese Control Conference S. 3138 - 3143
Hauptverfasser: Peng, Senran, Jia, Lijuan, Kanae, Shunshoku, Yang, Zi-jiang
Format: Tagungsbericht
Sprache:Englisch
Japanisch
Veröffentlicht: Technical Committee on Control Theory, Chinese Association of Automation 25.07.2022
Schlagworte:
Bias-compensation
diffusion networks
distributed networks
Estimation
recursive least squares
Simulation
sparse system identification
System identification
ISSN:1934-1768
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Zusammenfassung:In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give simulation results to verify that proposed algorithms have better estimation accuracy than other sparse RLS algorithms without bias compensation, it also proves that results are unbiased under input noises.
ISSN:1934-1768
DOI:10.23919/CCC55666.2022.9901566