Online Sparse Volterra System Identification Using Projections onto Weighted l(1) Balls
- Title
- Online Sparse Volterra System Identification Using Projections onto Weighted l(1) Balls
- Author
- 장준혁
- Keywords
- adaptive filtering; sparse Volterra systems; identification; projections
- Issue Date
- 2013-10
- Publisher
- IEICE
- Citation
- IEICE Transactions on Fundamentals of Electronics Communication and Computer Science, 2013, E96A(10), P.1980-1983
- 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.
- URI
- https://www.jstage.jst.go.jp/article/transfun/E96.A/10/E96.A_1980/_article
- ISSN
- 0916-8508; 1745-1337
- DOI
- 10.1587/transfun.E96.A.1980
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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