Hammerstein model identification using radial basis functions neural networks

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

5 Scopus citations

Abstract

A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2001 - International Conference, Proceedings
EditorsKurt Hornik, Georg Dorffner, Horst Bischof
PublisherSpringer Verlag
Pages951-956
Number of pages6
ISBN (Print)3540424865, 9783540446682
DOIs
StatePublished - 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2130
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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