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Neural networks for nonlinear modeling of dynamic systems: Design problems

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Design and Analysis of Experiments

We start from a brief review of artificial neural networks with external dynamics as models for nonlinear dynamic systems (NARX, NFIR ). We discuss problems arising in designing of such networks. In particular, we put emphasis on active learning, i.e., on iterative improvements of the Fisher information matrix. Furthermore, we propose random projections (applied to input and/or output signals) for increasing the robustness of model selection process.

This talk is part of the Isaac Newton Institute Seminar Series series.

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