Name
Title | ||
|---|---|---|
Online Sparse Volterra System Identification Using Projections Onto Weighted L(1) Balls |
Abstract | ||
|---|---|---|
In this paper, online sparse Volterra system identification is proposed. For that purpose, the conventional adaptive projection-based algorithm with weighted l(1) balls (APWL1) is revisited for nonlinear system identification, whereby the linear-in-parameters nature of Volterra systems is utilized. Compared with sparsity-aware recursive least squares (RLS) based algorithms, requiring higher computational complexity and showing faster convergence and lower steady-state error due to their long memory in time-invariant cases, the proposed approach yields better tracking capability in time-varying cases due to short-term data dependence in updating the weight. Also, when N is the number of sparse Volterra kernels and q is the number of input vectors involved to update the weight, the proposed algorithm requires O(qN) multiplication complexity and O(N log(2) N) sorting-operation complexity. Furthermore, sparsity-aware least mean-squares and affine projection based algorithms are also tested. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1587/transfun.E96.A.1980 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
adaptive filtering, sparse Volterra systems, identification, projections | Pattern recognition,Sparse approximation,Ball (bearing),Adaptive filter,Artificial intelligence,System identification,Mathematics | Journal |
Volume | Issue | ISSN |
E96A | 10 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taeho Jung | 1 | 462 | 36.24 |
| junghee kim | 2 | 6 | 1.44 |
| joonhyuk chang | 3 | 0 | 0.34 |
| sang won nam | 4 | 20 | 2.36 |