Recursive parameter identification of a class of nonlinear systems from noisy measurements

Kamal A.F. Moustafa, Hosam E. Emara-Shabaik

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

A model is proposed to identify the parameters of a class of stochastic nonlinear systems. The model structure is made up of two linear dynamic elements separated by a nonlinear static one. The nonlinear element is assumed to be of the polynomial type with known order. The identification is based on input/output data where the output is contaminated with measurement noise. The convergence analysis of the proposed recursive identification algorithm utilizes stochastic Lyapunov functions. Sufficient conditions for the almost sure convergence of the estimated parameters to the true ones are obtained.

Original languageEnglish
Pages (from-to)49-60
Number of pages12
JournalJVC/Journal of Vibration and Control
Volume6
Issue number1
DOIs
Publication statusPublished - 2000
Externally publishedYes

Keywords

  • Lyapunov functions
  • Parameter identification
  • Stochastic systems
  • Wiener-Hammerstein model

ASJC Scopus subject areas

  • Automotive Engineering
  • General Materials Science
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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