Identification of Wiener model using radial basis functions neural networks

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

2 Scopus citations

Abstract

A new method is introduced for the identification of Wiener model. The Wiener model consists of a linear dynamic block followed by a static nonlinearity. The nonlinearity and the linear dynamic part in the model are identified by using radial basis functions neural network (RBFNN) and autoregressive moving average (ARMA) model, respectively. The new algorithm makes use of the well known mapping ability of RBFNN. The learning algorithm based on least mean squares (LMS) principle is derived for the training of the identification scheme. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model simultaneously.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2002 - International Conference, Proceedings
EditorsJose R. Dorronsoro, Jose R. Dorronsoro
PublisherSpringer Verlag
Pages344-350
Number of pages7
ISBN (Print)9783540440741
DOIs
StatePublished - 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2415 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Identification of Wiener model using radial basis functions neural networks'. Together they form a unique fingerprint.

Cite this