On structure identification of parallel Wiener-Hammerstein models

In this paper, models composed of parallel branches of the Wiener-Hammerstein type are studied. Using a zero-mean stationary white Gaussian sequence as input to such models, their structure can be identified by considering the bispectrum of the output sequence. Simulation examples are included to illustrate the results of the pape

Structure identification of a class of non-linear systems using correlation and bispectrum approaches

The class of nonlinear systems studied in this paper is assumed to be modeled by block-cascades. Such cascades are composed of a dynamic linear block (L) in cascade with a zero-memory nonlinear block (N) followed by another dynamic linear block (L). This class of models is extensively used to represent nonlinear dynamic systems and is known in the literature as Wiener-Hammerstein models. Using a zero-mean stationary white Gaussian process as an input to such models, a structure identification criterion is developed based on the bispectrum and bicoherence of the output sequence only. A comparison between the developed criterion and other cross-correlation based criterion is given

On structure identification of parallel Wiener-Hammerstein models

In this paper, models composed of parallel branches of the Wiener-Hammerstein type are studied. Using a zero-mean stationary white Gaussian sequence as input to such models, their structure can be identified by considering the bispectrum of the output sequence. Simulation examples are included to illustrate the results of the pape