  Identification of nonlinear block-oriented models has been extensively
studied. The presence of the process noise, more precisely its location in the
block-oriented model influences essentially the development of a consistent
identification algorithm. The present work is proposed with the aim to localize
the process noise in the block-oriented model for accurate nonlinear modeling.
To this end, the response of a Wiener-Hammerstein system is theoretically
analyzed, the disturbance component in the output, caused by the process noise
preceding the static nonlinearity, is shown to be dependent on the input
signal. Inspired by such theoretical observation, a simple and new protocol is
developed to determine the location of the process noise with respect to the
static nonlinearity by using an input signal that is periodic, but
nonstationary within one period. In addition, the proposed technique is
promising to detect the type of certain static nonlinearity (e.g., dead-zone,
saturation). Finally, it is validated on a simulated example and a real-life
benchmark.
