Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 남상원 | - |
dc.date.accessioned | 2018-03-11T03:31:09Z | - |
dc.date.available | 2018-03-11T03:31:09Z | - |
dc.date.issued | 2013-10 | - |
dc.identifier.citation | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2013, E96A(10), P.1980-1983 | en_US |
dc.identifier.issn | 0916-8508 | - |
dc.identifier.issn | 1745-1337 | - |
dc.identifier.uri | https://www.jstage.jst.go.jp/article/transfun/E96.A/10/E96.A_1980/_article | - |
dc.description.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. | en_US |
dc.description.sponsorship | This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant no. 2012R1A1A2005378). | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN | en_US |
dc.subject | adaptive filtering | en_US |
dc.subject | sparse Volterra systems | en_US |
dc.subject | identification | en_US |
dc.subject | projections | en_US |
dc.title | Online Sparse Volterra System Identification Using Projections onto Weighted l(1) Balls | en_US |
dc.type | Article | en_US |
dc.relation.volume | E96A | - |
dc.identifier.doi | 10.1587/transfun.E96.A.1980 | - |
dc.relation.page | 1980-1983 | - |
dc.relation.journal | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | - |
dc.contributor.googleauthor | Jung, Tae-Ho | - |
dc.contributor.googleauthor | Kim, Jung-Hee | - |
dc.contributor.googleauthor | Chang, Joon-Hyuk | - |
dc.contributor.googleauthor | Nam, Sang Won | - |
dc.relation.code | 2013003612 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | swnam | - |
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