Name
Abstract | ||
|---|---|---|
High-dimensional system identification problems can be efficiently addressed based on tensor decompositions and modelling. In this paper, we design a recursive least-squares (RLS) algorithm tailored for the identification of trilinear forms, namely RLS-TF. In our framework, the trilinear form is related to the decomposition of a third-order tensor (of rank one). The proposed RLS-TF algorithm acts on the individual components of the global impulse response, thus being efficient in terms of both performance and complexity. Simulation results indicate that the proposed solution outperforms the conventional RLS algorithm (which handles only the global impulse response), but also the previously developed trilinear counterparts based on the least-mean-squares algorithm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/a13060135 | ALGORITHMS |
Keywords | DocType | Volume |
adaptive filters,recursive least-squares (RLS) algorithm,system identification,tensor decomposition,trilinear forms | Journal | 13 |
Issue | Citations | PageRank |
6 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camelia Elisei-Iliescu | 1 | 11 | 3.31 |
| Laura Dogariu | 2 | 6 | 3.92 |
| Constantin Paleologu | 3 | 227 | 35.46 |
| Jacob Benesty | 4 | 32 | 5.57 |
| Andrei Alexandru | 5 | 66 | 12.52 |
| Silviu Ciochina | 6 | 285 | 35.23 |