Abstract
In this paper we propose an identification method of nonlinear system. This later can be structured by Wiener models. The determination of nonlinear system parameters can be done using spectral analysis. The system nonlinearity is allowed to be noninvertible general shape nonlinearity but it must be approximated by a polynomial function. The polynomial degree n can vary from one interval to another. The linear dynamic element is not-necessarily parametric but BIBO stable. In this work, a spectral method is developed allowing the estimates of the complex frequency gain as well as the estimates of nonlinear block parameters the identification method is built using one stage.
Highlights
This nonlinear system identification has been an active research area, especially over the last two decade [1]-[3]
A Wiener model is one of most popular models. It consists of linear element followed by nonlinearity block (Fig. 1). This model is more difficult than Hammerstein model
Use Wiener models consist of series connection of linear block G(s) and nonlinear static element f(.)
Summary
This nonlinear system identification has been an active research area, especially over the last two decade [1]-[3]. Except of this assumption, the nonlinear element can be of general shape and noninvertible.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
