Abstract
The paper addresses the problem of Hammerstein–Wiener (N–L–N) system identification. The system is identified in so-called two-experiment approach. In passive experiment the system is excited with random noise, whereas in active experiment binary sequences are used. We present an algorithm with four consecutive stages, in which static nonlinear characteristics are recovered separately from the linear dynamic block. The proposed method uses both parametric and nonparametric identification tools. The estimates are based on kernel preselection of data and application of local least squares. Identification of output nonlinearity is processed under active experiment. We analyze the consistency of the proposed estimates under some a priori restrictions imposed on the excitation signal and system characteristics. Finally, we present a simple simulation example to demonstrate the behaviour of the algorithm.
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