A two-experiment approach to Wiener system identification

G. Bottegal, Ricardo Castro-Garcia, Johan A.K. Suykens

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
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We propose a new methodology for identifying Wiener systems using the data acquired from two separate experiments. In the first experiment, we feed the system with a sinusoid at a prescribed frequency and use the steady state response of the system to estimate the static nonlinearity. In the second experiment, the estimated nonlinearity is used to identify a model of the linear block, feeding the system with a persistently exciting input. We discuss both parametric and nonparametric approaches to estimate the static nonlinearity. In the parametric case, we show that modeling the static nonlinearity as a polynomial results into a fast least-squares based estimation procedure. In the nonparametric case, least squares support vector machines (LS-SVM) are employed to obtain a flexible model. The effectiveness of the method is demonstrated through numerical experiments.
Original languageEnglish
Pages (from-to)282-289
Number of pages8
Publication statusPublished - Jul 2018


  • System identification
  • Wiener systems
  • Experiment design
  • Least squares support vector machines


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