Online Sparse Volterra System Identification Using Projections onto Weighted l(1) Balls
- Authors
- Jung, Tae-Ho; Kim, Jung-Hee; Chang, Joon-Hyuk; Nam, Sang Won
- Issue Date
- Oct-2013
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Keywords
- adaptive filtering; sparse Volterra systems; identification; projections
- Citation
- IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, v.E96A, no.10, pp.1980 - 1983
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
- Volume
- E96A
- Number
- 10
- Start Page
- 1980
- End Page
- 1983
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/26633
- DOI
- 10.1587/transfun.E96.A.1980
- ISSN
- 0916-8508
- 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.
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