Initialization of nonlinear state-space models applied to the Wiener–Hammerstein benchmark

In this work a new initialization scheme for nonlinear state-space models is applied to the problem of identifying a Wiener-Hammerstein system on the basis of a set of real data. The proposed approach combines ideas from the statistical learning community with classic system identification methods. The results on the benchmark data are discussed and compared to the ones obtained by other related methods.