Identification of block-oriented nonlinear systems starting from linear approximations: a survey

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Block-oriented nonlinear models are popular in nonlinear system identification because of their advantages of being simple to understand and easy to use. Many different identification approaches were developed over the years to estimate the parameters of a wide range of block-oriented nonlinear models. One class of these approaches uses linear approximations to initialize the identification algorithm. The best linear approximation framework and the -approximation framework, or equivalent frameworks, allow the user to extract important information about the system, guide the user in selecting good candidate model structures and orders, and prove to be a good starting point for nonlinear system identification algorithms. This paper gives an overview of the different block-oriented nonlinear models that can be identified using linear approximations, and of the identification algorithms that have been developed in the past. A non-exhaustive overview of the most important other block-oriented nonlinear system identification approaches is also provided throughout this paper.
Original languageEnglish
Pages (from-to)272-292
Number of pages21
Publication statusPublished - Nov 2017
Externally publishedYes


  • Best linear approximation
  • Feedback
  • Hammerstein
  • Hammerstein–Wiener
  • Linear fractional representation
  • Maximum likelihood
  • Nonlinear systems
  • Parallel cascade
  • System identification
  • Wiener
  • Wiener–Hammerstein


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