  Most studies tackling hysteresis identification in the technical literature
follow white-box approaches, i.e. they rely on the assumption that measured
data obey a specific hysteretic model. Such an assumption may be a hard
requirement to handle in real applications, since hysteresis is a highly
individualistic nonlinear behaviour. The present paper adopts a black-box
approach based on nonlinear state-space models to identify hysteresis dynamics.
This approach is shown to provide a general framework to hysteresis
identification, featuring flexibility and parsimony of representation.
Nonlinear model terms are constructed as a multivariate polynomial in the state
variables, and parameter estimation is performed by minimising weighted
least-squares cost functions. Technical issues, including the selection of the
model order and the polynomial degree, are discussed, and model validation is
achieved in both broadband and sine conditions. The study is carried out
numerically by exploiting synthetic data generated via the Bouc-Wen equations.
