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The problem of data pre-filtering for nonparametric identification of Hammerstein system from short (finite) data set is considered. The two-stage method is proposed. First, the linear dynamic block is identified using instrumental variables technique, and the inverse of the obtained model is used for output filtering. Next, the standard procedure of(More)
A mixed, parametric–non-parametric routine for Hammerstein system identification is presented. Parameters of a non-linear characteristic and of ARMA linear dynamical part of Hammerstein system are estimated by least squares and instrumental variables assuming poor a priori knowledge about the random input and random noise. Both subsystems are identified(More)
A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammer-stein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the(More)
—A new, censored sample mean nonparametric identification algorithm for estimation of a nonlinear characteristic in Wiener system using properly preselected input–output data is proposed. Conditions imposed on the unknown characteristic are weak. In particular, its invertibility and global continuity are not required. The algorithm is based on computation(More)
A combined, parametric-nonparametric identification algorithm for a special case of NARMAX systems is proposed. The parameters of individual blocks are aggregated in one matrix (including mixed products of parameters). The matrix is estimated by an instrumental variables technique with the instruments generated by a nonparametric kernel method. Finally, the(More)