  Block-oriented nonlinear models are popular in nonlinear system
identification because of their advantages of being simple to understand and
easy to use. Many different identification approaches were developed over the
years to estimate the parameters of a wide range of block-oriented nonlinear
models. One class of these approaches uses linear approximations to initialize
the identification algorithm. The best linear approximation framework and the
$\epsilon$-approximation framework, or equivalent frameworks, allow the user to
extract important information about the system, guide the user in selecting
good candidate model structures and orders, and prove to be a good starting
point for nonlinear system identification algorithms. This paper gives an
overview of the different block-oriented nonlinear models that can be
identified using linear approximations, and of the identification algorithms
that have been developed in the past. A non-exhaustive overview of the most
important other block-oriented nonlinear system identification approaches is
also provided throughout this paper.
