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Recurrent Neural Networks are universal approximators
Anton Maximilian Schäfer1, Hans-Georg Zimmermann
1University of Osnabrück, Neuroinformatics Group, Albrechtstrasse 28, 49069 Osnabrück, Germany. Schaefer.Anton.Ext@siemens.com
International Journal of Neural Systems
|August 19, 2007
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.
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