  This paper studies the MINLIP estimator for the identification of Wiener
systems consisting of a sequence of a linear FIR dynamical model, and a
monotonically increasing (or decreasing) static function. Given $T$
observations, this algorithm boils down to solving a convex quadratic program
with $O(T)$ variables and inequality constraints, implementing an inference
technique which is based entirely on model complexity control. The resulting
estimates of the linear submodel are found to be almost consistent when no
noise is present in the data, under a condition of smoothness of the true
nonlinearity and local Persistency of Excitation (local PE) of the data. This
result is novel as it does not rely on classical tools as a 'linearization'
using a Taylor decomposition, nor exploits stochastic properties of the data.
It is indicated how to extend the method to cope with noisy data, and empirical
evidence contrasts performance of the estimator against other recently proposed
techniques.
