Recursive identification of Hammerstein systems with dead-zone nonlinearity in the presence of bounded noise

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)2394-2404
Journal / PublicationInternational Journal of Systems Science
Issue number11
Publication statusPublished - 18 Apr 2017


The existing identification algorithms for Hammerstein systems with dead-zone nonlinearity are restricted by the noise-free condition or the stochastic noise assumption. Inspired by the practical bounded noise assumption, an improved recursive identification algorithm for Hammerstein systems with dead-zone nonlinearity is proposed. Based on the system parametric model, the algorithm is derived by minimising the feasible parameter membership set. The convergence conditions are analysed, and the adaptive weighting factor and the adaptive covariance matrix are introduced to improve the convergence. The validity of this algorithm is demonstrated by two numerical examples, including a practical DC motor case.

Research Area(s)

  • Recursive identification, bounded noise, Hammerstein system, dead-zone nonlinearity, convergence analysis