Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 41st Chinese Control Conference, CCC 2022 |
| Editors | Zhijun Li, Jian Sun |
| Publisher | IEEE Computer Society |
| Pages | 3138-3143 |
| Number of pages | 6 |
| ISBN (Electronic) | 9789887581536 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 41st Chinese Control Conference, CCC 2022 - Hefei, China Duration: 25 Jul 2022 → 27 Jul 2022 |
Publication series
| Name | Chinese Control Conference, CCC |
|---|---|
| Volume | 2022-July |
| ISSN (Print) | 1934-1768 |
| ISSN (Electronic) | 2161-2927 |
Conference
| Conference | 41st Chinese Control Conference, CCC 2022 |
|---|---|
| Country/Territory | China |
| City | Hefei |
| Period | 25/07/22 → 27/07/22 |
Keywords
- Bias-compensation
- diffusion networks
- distributed networks
- recursive least squares
- sparse system identification
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Peng, S., Jia, L., Kanae, S., & Yang, Z. J. (2022). Bias-compensated Sparse RLS Algorithms Over Distributed Networks. In Z. Li, & J. Sun (Eds.), Proceedings of the 41st Chinese Control Conference, CCC 2022 (pp. 3138-3143). (Chinese Control Conference, CCC; Vol. 2022-July). IEEE Computer Society. https://doi.org/10.23919/CCC55666.2022.9901566