  This paper discusses a novel initialization algorithm for the estimation of
nonlinear state-space models. Good initial values for the model parameters are
obtained by identifying separately the linear dynamics and the nonlinear terms
in the model. In particular, the nonlinear dynamic problem is transformed into
an approximate static formulation, and simple regression methods are applied to
obtain the solution in a fast and efficient way. The proposed method is
validated by means of two measurement examples: the Wiener-Hammerstein
benchmark problem, and the identification of a crystal detector.
