Name
Title | ||
|---|---|---|
A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm For System Identification |
Abstract | ||
|---|---|---|
The performance of the recursive least-squares (RI,S) algorithm is governed by the forgetting factor. This parameter leads to a compromise between 1) the tracking capabilities and 2) the misadjustment and stability. In this letter, a variable forgetting factor RLS (VFF-RLS) algorithm is proposed for system identification. In general, the output of the unknown system is corrupted by a noise-like signal. This signal should be recovered in the error signal of the adaptive filter after this one converges to the true solution. This condition is used to control the value of the forgetting factor. The simulation results indicate the good performance and the robustness of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/LSP.2008.2001559 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | Field | DocType |
Adaptive filtering, echo cancellation, recursive least-squares (RLS) | Convergence (routing),Least squares,Signal processing,Mathematical optimization,Control theory,Algorithm,Robustness (computer science),Adaptive filter,System identification,Gaussian noise,Mathematics,Recursive least squares filter | Journal |
Volume | ISSN | Citations |
15 | 1070-9908 | 42 |
PageRank | References | Authors |
2.40 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. Paleologu | 1 | 169 | 9.03 |
| Jacob Benesty | 2 | 1941 | 146.01 |
| S. Ciochina | 3 | 169 | 9.03 |