ℓ1-regularized recursive total least squares based sparse system identification for the error-in-variables
In this paper an ℓ 1 -regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse,...
Saved in:
| Published in: | SpringerPlus Vol. 5; no. 1 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cham
Springer International Publishing
31.08.2016
|
| Subjects: | |
| ISSN: | 2193-1801 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In this paper an
ℓ
1
-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed
ℓ
1
-RTLS algorithm is an RLS like iteration using the
ℓ
1
regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed
ℓ
1
-regularized RTLS for the sparse system identification setting. |
|---|---|
| AbstractList | In this paper an
ℓ
1
-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed
ℓ
1
-RTLS algorithm is an RLS like iteration using the
ℓ
1
regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed
ℓ
1
-regularized RTLS for the sparse system identification setting. |
| Author | Pang, Hee-Suk Lim, Jun-seok |
| Author_xml | – sequence: 1 givenname: Jun-seok surname: Lim fullname: Lim, Jun-seok email: jslim@sejong.ac.kr organization: Department of Electronics Engineering, Sejong University – sequence: 2 givenname: Hee-Suk surname: Pang fullname: Pang, Hee-Suk organization: Department of Electronics Engineering, Sejong University |
| BookMark | eNotkE1OwzAUhC0EEqX0AOx8AYOfkzjOElX8SZXYwNqyk-fiKiTFz60Ea27ADTkJqcpsZjGjGem7YKfDOCBjVyCvAYy-oVJKXQoJWhSgpNAnbKagKQQYCedsQbSRk3QNZS1nrP_9_gGRcL3rXYpf2PGE7S5R3CPPY3Y979FR5vSxcwmJe0dTh7YuEXL6pIzvPHY45Bhi63IcBx7GxPMbckxpTCIOYj8tO98jXbKz4HrCxb_P2ev93cvyUayeH56WtytB0JgsOuNrj62SpvSoq9qbUEFwrmqDKVrQplRYd7KaQgwKPDbBtcrVvglQtrUp5kwdd2mb4rDGZDfjLg3TpQVpD5jsEZOdMNkDJquLPwlwYzo |
| Cites_doi | 10.1109/TSP.2004.837408 10.1049/iet-spr.2010.0083 10.1109/LSP.2009.2024736 10.1109/LSP.2011.2159373 10.1016/j.sigpro.2011.02.013 10.1109/TSP.2011.2109956 10.1109/78.705421 10.1137/0717073 10.1109/TSP.2010.2048103 10.1109/78.275601 10.1016/j.laa.2012.10.032 10.2514/1.6276 10.1002/acs.2635 10.1109/TSP.2010.2046897 10.1016/j.dsp.2015.02.018 10.1109/TSP.2015.2405492 10.1109/TSP.2014.2301135 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2016 |
| Copyright_xml | – notice: The Author(s) 2016 |
| DBID | C6C |
| DOI | 10.1186/s40064-016-3120-6 |
| DatabaseName | Springer Nature OA Free Journals |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2193-1801 |
| ExternalDocumentID | 10_1186_s40064_016_3120_6 |
| GroupedDBID | -A0 0R~ 4.4 40G 53G 5VS 7X2 7XC 8CJ 8FE 8FG 8FH AAKKN ABDBF ABEEZ ABJCF ACACY ACGFO ACGFS ACIWK ACPRK ACUHS ACULB ADBBV ADINQ ADRAZ AEGXH AENEX AEUYN AFGXO AFKRA AFRAH AHBYD AHSBF AHYZX AIAGR ALMA_UNASSIGNED_HOLDINGS AMKLP AOIJS ARAPS ATCPS BAWUL BBNVY BENPR BGLVJ BHPHI BKSAR C24 C6C CCPQU CZ9 D1I D1J D1K DIK EBS EJD GROUPED_DOAJ GX1 HCIFZ HH5 HYE HZ~ K6- K6V K7- KB. KC. KQ8 L6V LK5 LK8 M0K M48 M7P M7R M7S M~E OK1 P62 PATMY PCBAR PDBOC PGMZT PTHSS PYCSY RNS RPM RSV SHS SOJ |
| ID | FETCH-LOGICAL-s198t-d8b7bec2084be657b8f51faa5cf83c16842e7d05be6ef21be9fac2a7b9f14c783 |
| IEDL.DBID | C24 |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000391794900002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Fri Feb 21 02:35:35 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | RLS Convex regularization TLS 1-norm Adaptive filter Sparsity |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-s198t-d8b7bec2084be657b8f51faa5cf83c16842e7d05be6ef21be9fac2a7b9f14c783 |
| OpenAccessLink | https://link.springer.com/10.1186/s40064-016-3120-6 |
| ParticipantIDs | springer_journals_10_1186_s40064_016_3120_6 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-08-31 |
| PublicationDateYYYYMMDD | 2016-08-31 |
| PublicationDate_xml | – month: 08 year: 2016 text: 2016-08-31 day: 31 |
| PublicationDecade | 2010 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham |
| PublicationTitle | SpringerPlus |
| PublicationTitleAbbrev | SpringerPlus |
| PublicationYear | 2016 |
| Publisher | Springer International Publishing |
| Publisher_xml | – name: Springer International Publishing |
| References | Soijer (CR20) 2004; 27 Davila (CR8) 1994; 42 Arablouei, Werner, Dogancay (CR2) 2014; 62 Dunne, Williamson (CR11) 2004; 52 Golub, Van Loan (CR15) 1980; 17 CR6 Feng, Bao, Jiao (CR14) 1998; 46 Arablouei, Dogancay, Werner (CR3) 2015; 63 Choi, Lim, Sung (CR7) 2005; 3496 Eksioglu (CR12) 2011; 5 Angelosante, Bazerque, Giannakis (CR1) 2010; 58 Babadi, Kalouptsidis, Tarokh (CR5) 2010; 58 Dumitrescu (CR9) 2013; 438 CR10 Moon, Stirling (CR19) 2000 Tanc (CR21) 2015; 40 Arablouei (CR4) 2016 Gu, Jin, Mei (CR16) 2009; 16 Kalouptsidis, Mileounis, Babadi, Tarokh (CR17) 2011; 91 Eksioglu, Tanc (CR13) 2011; 18 Lim, Pang (CR18) 2016; 30 Zhu, Leus, Giannakis (CR22) 2011; 59 |
| References_xml | – volume: 52 start-page: 3345 year: 2004 end-page: 3356 ident: CR11 article-title: Analysis of gradient algorithms for TLS-based adaptive IIR filters publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2004.837408 – volume: 5 start-page: 480 year: 2011 end-page: 487 ident: CR12 article-title: Sparsity regularized RLS adaptive filtering publication-title: IET Signal Process doi: 10.1049/iet-spr.2010.0083 – year: 2000 ident: CR19 publication-title: Mathematical methods and algorithm for signal processing – volume: 16 start-page: 774 year: 2009 end-page: 777 ident: CR16 article-title: Norm constraint LMS algorithm for sparse system identification publication-title: IEEE Signal Process Lett doi: 10.1109/LSP.2009.2024736 – volume: 18 start-page: 470 year: 2011 end-page: 473 ident: CR13 article-title: RLS algorithm with convex regularization publication-title: IEEE Signal Process Lett doi: 10.1109/LSP.2011.2159373 – volume: 91 start-page: 1910 year: 2011 end-page: 1919 ident: CR17 article-title: Adaptive algorithms for sparse system identification publication-title: Signal Process doi: 10.1016/j.sigpro.2011.02.013 – volume: 59 start-page: 2002 year: 2011 end-page: 2016 ident: CR22 article-title: Sparsity-cognizant total least-squares for perturbed compressive sampling publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2011.2109956 – volume: 46 start-page: 2122 year: 1998 end-page: 2130 ident: CR14 article-title: Total least mean squares algorithm publication-title: IEEE Trans Signal Process doi: 10.1109/78.705421 – volume: 17 start-page: 883 year: 1980 end-page: 893 ident: CR15 article-title: An analysis of the total least squares problem publication-title: SIAM J Numer Anal doi: 10.1137/0717073 – volume: 58 start-page: 4013 year: 2010 end-page: 4025 ident: CR5 article-title: SPARLS: the sparse RLS algorithm publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2010.2048103 – volume: 42 start-page: 268 year: 1994 end-page: 280 ident: CR8 article-title: An efficient recursive total least squares algorithm for FIR adaptive filtering publication-title: IEEE Trans Signal Process doi: 10.1109/78.275601 – ident: CR10 – volume: 438 start-page: 2661 year: 2013 end-page: 2674 ident: CR9 article-title: Sparse total least squares: analysis and greedy algorithms publication-title: Linear Algebra Appl doi: 10.1016/j.laa.2012.10.032 – volume: 27 start-page: 501 year: 2004 end-page: 503 ident: CR20 article-title: Sequential computation of total least-squares parameter estimates publication-title: J Guidance doi: 10.2514/1.6276 – volume: 30 start-page: 664 year: 2016 end-page: 673 ident: CR18 article-title: Mixed norm regularized recursive total least squares for group sparse system identification publication-title: Int J Adapt Control Signal Process doi: 10.1002/acs.2635 – volume: 58 start-page: 3436 year: 2010 end-page: 3447 ident: CR1 article-title: Online adaptive estimation of sparse signals: where RLS meets the l1-norm publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2010.2046897 – ident: CR6 – volume: 3496 start-page: 558 year: 2005 end-page: 565 ident: CR7 article-title: An efficient recursive total least squares algorithm for training multilayer feedforward neural networks publication-title: LNCS – volume: 40 start-page: 176 year: 2015 end-page: 180 ident: CR21 article-title: Sparsity regularized recursive total least-squares publication-title: Digital Signal Process doi: 10.1016/j.dsp.2015.02.018 – volume: 63 start-page: 1941 year: 2015 end-page: 1949 ident: CR3 article-title: Recursive total least-squares algorithm based on inverse power method and dichotomous coordinate-descent iterations publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2015.2405492 – year: 2016 ident: CR4 article-title: Fast reconstruction algorithm for perturbed compressive sensing based on total least-squares and proximal splitting publication-title: Signal Process – volume: 62 start-page: 1256 year: 2014 end-page: 1264 ident: CR2 article-title: Analysis of the gradient-decent total least-squares algorithm publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2014.2301135 |
| SSID | ssj0000671470 |
| Score | 2.0722866 |
| Snippet | In this paper an
ℓ
1
-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least... |
| SourceID | springer |
| SourceType | Publisher |
| SubjectTerms | Engineering Humanities and Social Sciences multidisciplinary Science Science (multidisciplinary) |
| Title | ℓ1-regularized recursive total least squares based sparse system identification for the error-in-variables |
| URI | https://link.springer.com/article/10.1186/s40064-016-3120-6 |
| Volume | 5 |
| WOSCitedRecordID | wos000391794900002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources databaseCode: M~E dateStart: 20120101 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 99991231 titleUrlDefault: https://road.issn.org omitProxy: false ssIdentifier: ssj0000671470 providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database databaseCode: P5Z dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: https://search.proquest.com/hightechjournals omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Agricultural Science Database databaseCode: M0K dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: https://search.proquest.com/agriculturejournals omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database databaseCode: M7P dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: http://search.proquest.com/biologicalscijournals omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database (ProQuest) databaseCode: K7- dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: http://search.proquest.com/compscijour omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database databaseCode: PCBAR dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: https://search.proquest.com/eaasdb omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database databaseCode: M7S dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: http://search.proquest.com omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database databaseCode: PATMY dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: http://search.proquest.com/environmentalscience omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database databaseCode: KB. dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: http://search.proquest.com/materialsscijournals omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central databaseCode: BENPR dateStart: 20120301 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20180228 titleUrlDefault: https://www.proquest.com/central omitProxy: false ssIdentifier: ssj0000671470 providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerOpen databaseCode: C24 dateStart: 20121201 customDbUrl: isFulltext: true eissn: 2193-1801 dateEnd: 20171231 titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 omitProxy: false ssIdentifier: ssj0000671470 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELagZYABaAHxrDwwgJBFnTiJM0LViqmqEEjdIscPKRJKhZ12YOYf8A_5JdiuIyHBArOTU3SXe_m-uwPgUmIexywmiCgikHMBiLFcoFyKjMshj5liftlENp3S-TyfhT5u06Ld25Kkt9RerWl6a4hznzb1Ta3dcDnPJugmmOYOxzcKLQ7B_GKStRXMX9_8UfX0zmSy96_P2Ae7IXaEd2th98CGrPtg59tEwT7oBV018CoMlL4-AC-f7x8Yab91XldvUkDtbtkdcB02Cxt9wxe3wQea16XrRoLOswloTY02Eq5HPcNKBFiRlyS0oS60oSOUWi80qmq0spRdF5Y5BM-T8dPoAYUtC8jgnDZI0DKzgoyGlJQyTbKSqgQrxhKuaMyxq9PJTAwTeyhVhEuZK8YjlpW5woRnND4CnXpRy2MAIyK4kqK054JE3NLAIoktoZTFmFN-Am5anhZBVUzhsxCaFmvGFg5x5hhbpKd_evoMbEdOKP669xx0Gr2UF2CLr5rK6AHo3o-ns8eB_1W-AM7Xv8I |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LSwMxEA5aBfWgtiq-zcGDIsFmN7ubPUqxVKzFQ4XelmweUChbTNoePPsP_If-EpM0C4Je9JzdYZnZeWW-mQHgUmIexywmiCgikHMBiLFcoFyKjMs2j5liftlENhjQ0Sh_Dn3cpka71yVJb6m9WtP01hDnPm3qm1q74XKeVbDmJq84HF8ntDgE84tJVlcwf33zR9XTO5Puzr8-Yxdsh9gR3i2F3QQrsmqBrW8TBVugGXTVwKswUPp6D0w-3z8w0n7rvB6_SQG1u2V3wHU4m9roG07cBh9oXueuGwk6zyagNTXaSLgc9QzHIsCKvCShDXWhDR2h1Hqq0bhCC0vZdWGZffDSvR92eihsWUAG53SGBC0zK8ioTUkp0yQrqUqwYizhisYcuzqdzEQ7sYdSRbiUuWI8YlmZK0x4RuMD0KimlTwEMCKCKylKey5IxC0NLJLYEkpZjDnlR-Cm5mkRVMUUPguhabFkbOEQZ46xRXr8p6cvwEZv-NQv-g-DxxOwGTkB-avfU9CY6bk8A-t8MRsbfe5_ly_PzsEN |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR1NS8MwNOgU0YO6qfhtDh4UCVvatE2PMh2KMnZQ2K2k-YDC6DTpdvDsP_Af-ktM2gwEvYjntI_wvl_eFwDnEvMwZCFBRBGBnAlAjKUCpVIkXPZ4yBSrl00kwyEdj9OR33NqFtXui5Rk09PgpjSVVfdFqEbEadw1xJlSGwbHVoe4-GcZrLiElGPxvm938KoYk2SRzfz1zx8Z0NqwDLb-faVtsOl9SnjdMEEbLMmyAza-TRrsgLaXYQMv_KDpyx0w-Xz_wEjX2-h18SYF1O713RW0w2pqvXI4cZt9oHmduS4l6CyegFYFaSNhMwIaFsKXG9UUhtYFhtalhFLrqUZFieYWsuvOMrvgeXD71L9DfvsCMjilFRI0TyyBgx4luYyjJKcqwoqxiCsacuzydzIRvcgeShXgXKaK8YAleaow4QkN90CrnJZyH8CACK6kyO25IAG3MLCIQgsoZiHmlB-AqwV-My9CJqujExpnDWIzV4nmEJvFh3_6-gysjW4G2eP98OEIrAeOPvWL8DFoVXomT8Aqn1eF0ac153wBNpfJ8Q |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E2%84%931-regularized+recursive+total+least+squares+based+sparse+system+identification+for+the+error-in-variables&rft.jtitle=SpringerPlus&rft.au=Lim%2C+Jun-seok&rft.au=Pang%2C+Hee-Suk&rft.date=2016-08-31&rft.pub=Springer+International+Publishing&rft.eissn=2193-1801&rft.volume=5&rft.issue=1&rft_id=info:doi/10.1186%2Fs40064-016-3120-6&rft.externalDocID=10_1186_s40064_016_3120_6 |