期刊
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
卷 17, 期 4, 页码 253-263出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065707001111
关键词
dynamical systems; system identification; recurrent neural networks; universal approximation
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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