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- W2024450772 abstract "Recurrent Neural Networks (RNN) have been developed for a better understanding and analysis of open dynamical systems. Still the question often arises if RNN are able to map every open dynamical system, which would be desirable for a broad spectrum of applications. In this article we give a proof for the universal approximation ability of RNN in state space model form and even extend it to Error Correction and Normalized Recurrent Neural Networks." @default.
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- W2024450772 date "2007-08-01" @default.
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- W2024450772 title "RECURRENT NEURAL NETWORKS ARE UNIVERSAL APPROXIMATORS" @default.
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- W2024450772 doi "https://doi.org/10.1142/s0129065707001111" @default.
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