Nonparametric Identification of Generalized Hammerstein Models

Abstract The popular Hammerstein model consists of a static (zero-memory) non-linearity followed in series by a linear dynamic model. This paper considers an extension of this structure in which the static nonlinearity is replaced by a finite-memory nonlinearity. The result is a large class of fading memory models that includes many interesting special cases. We consider the nonparametric identification problem, building on previous results for nonparametric Hammerstein model identification, emphasizing interactions between (partial) model structure specifications, input sequence characteristics, and modeling error/distribution assumptions.

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