A Fast Nonlinear Model Identification Method

Kang Li, Jian Xun Peng, George Irwin

Research output: Contribution to journalArticle

200 Citations (Scopus)

Abstract

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
Original languageEnglish
Pages (from-to)1211-1216
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume50 (8)
Issue number8
DOIs
Publication statusPublished - Aug 2005

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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