Recurrent Neural Networks are universal approximators.
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.
Schäfer
Anton Maximilian
AM
University of Osnabrück, Neuroinformatics Group, Albrechtstrasse 28, 49069 Osnabrück, Germany. Schaefer.Anton.Ext@siemens.com
Zimmermann
Hans-Georg
HG
D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors