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Block-oriented nonlinear models are popular in nonlinear systemidentification because of their advantages of being simple to understand andeasy to use. Many different identification approaches were developed over theyears to estimate the parameters of a wide range of block-oriented nonlinearmodels. One class of these approaches uses linear approximations to initializethe identification algorithm. The best linear approximation framework and the$\epsilon$-approximation framework, or equivalent frameworks, allow the user toextract important information about the system, guide the user in selectinggood candidate model structures and orders, and prove to be a good startingpoint for nonlinear system identification algorithms. This paper gives anoverview of the different block-oriented nonlinear models that can beidentified using linear approximations, and of the identification algorithmsthat have been developed in the past. A non-exhaustive overview of the mostimportant other block-oriented nonlinear system identification approaches isalso provided throughout this paper.

Identification of block-oriented nonlinear systems starting from linear approximations: A survey