Abstract
Abstract The article deals with an experimental comparison of different architectures for modelling of dynamic systems from input/output data with prior knowledge on the structure of the system. The following models are compared: linear discrete autoregressive model (ARX) and three variants of the nonlinear autoregressive (NARX) model: general nonlinear model, fuzzy NARX and neural network NARX model. Models were tested using real data obtained from the laboratory magnetic levitation model. Following aspects have been studied: parameter convergence, accuracy of the modelling and influence of the initial conditions.
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