A novel two-stage estimation algorithm for nonlinear Hammerstein–Wiener systems from noisy input and output data

摘要: Abstract This paper investigates the identification problem of Hammerstein–Wiener errors-in-variable systems where measurement errors system input and output are either temporally white or have relatively short memory size compared to data length, but corresponding variances unknown. A two-stage algorithm is developed estimate unknown parameters with first stage employing a modified bias-eliminating least squares algorithm, followed by singular value decomposition in second stage. Our proposed estimator shown be asymptotically unbiased. The simulation result shows effectiveness algorithm.