Abstract
In this paper the impact of nonlinear distortions on the linear system identification framework is studied. In the first part the nonlinear system is replaced by a linear model plus a nonlinear noise source. The properties of this representation are studied. Next a method to detect, qualify and quantify the nonlinear distortions is presented. In the second part, the (non)-parametric identification of the best linear approximation is studied. In the last part, the linear modelling approach is extended towards nonlinear modelling. A fast approximate nonlinear modelling framework is set up that is a natural extension of the linear framework, and bridges the gap between the linear and the nonlinear identification approaches.
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