Name
Abstract | ||
|---|---|---|
This contribution presents a new algorithm of the LMS family, derived from a novel orthogonality condition that holds for overdetermined problems that include an instrumental variable. This instrumental variable can be used to introduce higher-order statistics information. The convergence of the MSE for this new algorithm is theoretically studied, together with its superior performance when compared with other similar algorithms, under quite general hypotheses. The algorithm is then applied to the blind identification of moving average models; simulation results verify the analysis. |
Year | Venue | Keywords |
|---|---|---|
2004 | EUSIPCO | higher order statistics,least mean squares methods,moving average processes,averaged lms algorithm,blind identification,generalised lms algorithm,least mean square algorithm,moving average model,orthogonality condition,overdetermined lms algorithm |
Field | DocType | ISBN |
Convergence (routing),Overdetermined system,Instrumental variable,Algorithm,Orthogonality,Moving average,Mathematics | Conference | 978-320-0001-65-7 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alameda-Hernandez, E. | 1 | 105 | 7.30 |
| D. P. Ruiz | 2 | 10 | 3.76 |
| D. Blanco | 3 | 7 | 2.96 |
| D. C. Mclernon | 4 | 126 | 15.01 |
| Maria Carmen Carrion Perez | 5 | 0 | 0.34 |