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Identification methods of Hammerstein nonelinear CARAR systems

A recursive generalized least squares and a generalized stochastic gradient algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks followed by linear dynamical blocks described by CARAR models (HCARAR models). The basic idea is to replace the unmeasurable noise terms in the information vectors with their estimates and to compute the noise estimates through different methods. The simulation results show the performance of the proposed algorithms.