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dc.contributor.authorSayin, M. O.en_US
dc.contributor.authorYilmaz, Y.en_US
dc.contributor.authorDemir, A.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2016-02-08T11:01:20Z
dc.date.available2016-02-08T11:01:20Z
dc.date.issued2015en_US
dc.identifier.issn0165-1684
dc.identifier.urihttp://hdl.handle.net/11693/26544
dc.description.abstractWe propose novel adaptive filtering algorithms based on the mean-fourth error objective while providing further improvements on the convergence performance through proportionate update. We exploit the sparsity of the system in the mean-fourth error framework through the proportionate normalized least mean fourth (PNLMF) algorithm. In order to broaden the applicability of the PNLMF algorithm to dispersive (non-sparse) systems, we introduce the Krylov-proportionate normalized least mean fourth (KPNLMF) algorithm using the Krylov subspace projection technique. We propose the Krylov-proportionate normalized least mean mixed norm (KPNLMMN) algorithm combining the mean-square and mean-fourth error objectives in order to enhance the performance of the constituent filters. Additionally, we propose the stable-PNLMF and stable-KPNLMF algorithms overcoming the stability issues induced due to the usage of the mean fourth error framework. Finally, we provide a complete performance analysis, i.e.; the transient and the steady-state analyses, for the proportionate update based algorithms, e.g.; the PNLMF, the KPNLMF algorithms and their variants; and analyze their tracking performance in a non-stationary environment. Through the numerical examples, we demonstrate the match of the theoretical and ensemble averaged results and show the superior performance of the introduced algorithms in different scenarios.en_US
dc.language.isoEnglishen_US
dc.source.titleSignal Processingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.sigpro.2014.10.015en_US
dc.subjectKrylov subspaceen_US
dc.subjectNLMFen_US
dc.subjectProportional updateen_US
dc.subjectSteady-state analysisen_US
dc.subjectTracking performanceen_US
dc.subjectTransient analysisen_US
dc.subjectAlgorithmsen_US
dc.titleThe Krylov-proportionate normalized least mean fourth approach: formulation and performance analysisen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage1en_US
dc.citation.epage13en_US
dc.citation.volumeNumber109en_US
dc.identifier.doi10.1016/j.sigpro.2014.10.015en_US
dc.publisherElsevier BVen_US


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