Separable inputs for the identification of block-oriented nonlinear systems

In this paper, noniterative procedures for the identification of block-oriented nonlinear models, consisting of the interconnection of linear time invariant systems and static nonlinearities are presented. The proposed algorithms are based on the idea of separable input, that is an input which is able to separate the linear and the nonlinear blocks in some sense in identification. Two different block-oriented nonlinear models are considered in this paper, the Hammerstein model and the Wiener model. A simulated example is presented in order to show the effectiveness of the proposed procedures.