  Balancing the model complexity and the representation capability towards the
process to be captured remains one of the main challenges in nonlinear system
identification. One possibility to reduce model complexity is to impose
structure on the model representation. To this end, this work considers the
linear fractional representation framework. In a linear fractional
representation the linear dynamics and the system nonlinearities are modeled by
two separate blocks that are interconnected with one another. This results in a
structured, yet flexible model structure. Estimating such a model directly from
input-output data is not a trivial task as the involved optimization is
nonlinear in nature. This paper proposes an initialization scheme for the model
parameters based on the best linear approximation of the system and shows that
this approach results in high quality models on a set of benchmark data sets.
