Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling

Qilin Liu, Feiyan Chen, Feng Ding*, Ahmed Alsaedi, Tasawar Hayat

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein-Wiener nonlinear systems based on non-uniform sampling. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model-based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.

Original languageEnglish
Pages (from-to)1612-1632
Number of pages21
JournalInternational Journal of Adaptive Control and Signal Processing
Issue number8
Publication statusPublished - Aug 2021


  • Hammerstein-Wiener model
  • auxiliary model
  • hierarchical identification
  • non-uniform sampling
  • nonlinear systems
  • parameter estimation

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