Mimo wiener model identification using radial basis functions neural networks

Syed Saad Azhar Ali*, Hussain N. Al-Duwaish

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper presents a novel technique for the identification of the nonlinear multi-input multi-output (MIMO) Wiener Model, consisting of linear dynamics in cascade with static nonlinearities. The ARMA/RBFNN structure presented in [1] is exteneded for MIMO case. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model based on least mean squares (LMS). The identification of both linear and nonlinear parts is done simultaneously as compared to the other indirect approaches.

Original languageEnglish
Article number412-164
Pages (from-to)74-78
Number of pages5
JournalProceedings of the IASTED International Conference on Modelling, Identification and Control
Volume23
StatePublished - 2004

Keywords

  • LMS
  • MIMO
  • RBFNN
  • Wiener Model

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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