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Direction-dependent dynamic systems are defined, and Wiener models for them are described. For first-order systems with pseudo-random binary inputs, optimising the model parameters by cross-correlation function matching methods based on analysis gives excellent results. For first-order systems with inverse-repeat pseudo-random binary inputs, optimisation by(More)
—The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat(More)
The paper deals with the identification of linear systems when they are in a pathway in series with a saturation nonlinearity. The objective is to estimate the parameters of the linear system and to obtain some characterisation of the nonlinearity, using only the input and output signals of the pathway. Both Wiener structures and Hammerstein structures are(More)