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Kernel adaptive Hammerstein filter

  • Xi'an Jiaotong University
  • Xi'an University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

To identify Hammerstein systems, a variety of Hammerstein filters have been proposed. However, most of them assume the nonlinear part in Hammerstein systems to be polynomial in the process of modeling, which restricts their applicability in many practical situations. In this paper, a simple kernel adaptive filter (KAF) called kernel least mean square (KLMS) combined with coherence criterion (CC) is used to approximate the nonlinear part of a Hammerstein system, resulting in the kernel adaptive Hammerstein filter (KAHF). The KAHF can identify various Hammerstein systems well without any prior knowledge of nonlinear part. Simulation results confirm the desirable performance of the new method.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages504-508
Number of pages5
ISBN (Electronic)9789082797015
DOIs
StatePublished - 29 Nov 2018
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference
Volume2018-September
ISSN (Print)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
Country/TerritoryItaly
CityRome
Period3/09/187/09/18

Keywords

  • Hammerstein system identification
  • Infinite impulse response system
  • Kernel adaptive filter

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