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Modeling of direction-dependent processes using Wiener models and neural networks with nonlinear output error structure

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Abstract

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 pseudorandom binary signal and a multisine (sum of harmonics) signal. Experimental results on a real system, namely an electronic nose system, are also presented to illustrate the applicability of the techniques discussed

Topics: TK
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
OAI identifier: oai:wrap.warwick.ac.uk:8400
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