Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0165-1684(02)00449-8
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dc.titleAn accelerated Gauss-Seidel method for inverse modeling
dc.contributor.authorNg, T.M.
dc.contributor.authorFarhang-Boroujeny, B.
dc.contributor.authorGarg, H.K.
dc.date.accessioned2014-06-17T02:37:41Z
dc.date.available2014-06-17T02:37:41Z
dc.date.issued2003-03
dc.identifier.citationNg, T.M., Farhang-Boroujeny, B., Garg, H.K. (2003-03). An accelerated Gauss-Seidel method for inverse modeling. Signal Processing 83 (3) : 517-529. ScholarBank@NUS Repository. https://doi.org/10.1016/S0165-1684(02)00449-8
dc.identifier.issn01651684
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54977
dc.description.abstractInverse modeling is an application for adaptive filters that has found extensive use in many engineering disciplines. In this paper, we consider the problem of finding inverse models in the area of channel equalization, and adaptive control systems. First, the problem is formulated in a general setting as a standard least squares problem. With this, the inverse model can be found using any one of the many well established least squares methods. One such method is the classical Gauss-Seidel method. As the Gauss-Seidel method has the limitation of being slow in converging to the required solution when applied to inverse modeling, we propose a new acceleration technique to speed up its convergence. © 2002 Elsevier Science B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0165-1684(02)00449-8
dc.sourceScopus
dc.subjectAdaptive control system
dc.subjectChannel equalizer
dc.subjectGauss-Seidel method
dc.subjectLinear acceleration
dc.subjectSOR method
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/S0165-1684(02)00449-8
dc.description.sourcetitleSignal Processing
dc.description.volume83
dc.description.issue3
dc.description.page517-529
dc.description.codenSPROD
dc.identifier.isiut000181119700005
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