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