Adaptive matching pursuit of NARX models with spline basis functions

Armin Shmilovici, Jose Aguilar-Martin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with eXogenous inputs (NARX) models of a nonlinear system. This algorithm, which is derived from the Matching Pursuit algorithm, is used for the online identification of nonlinear dynamic systems. The NARX model is expanded into a sum of non-orthogonal spline basis functions. The convergence of the algorithm is proved for certain signal assumptions. Simulation experiments are provided for examples previously solved in the literature.

Original languageEnglish
Pages (from-to)879-888
Number of pages10
JournalInternational Journal of Systems Science
Issue number8
StatePublished - 1 Jan 1999

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications


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