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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)