Abstract
In this paper, a novel algorithm is developed for identifying Hammerstein model. The static nonlinear function is characterized by function link artificial neural network (FLANN) and the linear dynamic subsystem by an ARMA model. The utilization of FLANN can not only result in a simple and effective representation of static nonlinearity but also simplify the learning algorithm. A two-step procedure is adopted to identify Hammerstein model by using a specially designed input signal, which separates the identification of linear part from that of nonlinear part. Levenberg–Marquart algorithm is used to learn the weights of FLANN. Simulation examples demonstrate the effectiveness of the proposed method.
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