The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
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.
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Citations
Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
Cited by
Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093
Cognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535
Deep learning rainfall–runoff predictions of extreme events
Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., & Nearing, G. S. (2022) · Hydrology and Earth System Sciences · doi:10.5194/hess-26-3377-2022 · Cited by 396
Hard criteria for empirical theories of consciousness
Doerig, A., Schurger, A., & Herzog, M. H. (2021) · Cognitive Neuroscience · doi:10.1080/17588928.2020.1772214 · Cited by 194
Falsification and consciousness
Kleiner, J., & Hoel, E. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab001 · Cited by 46
Metadata
- Authors
- SCHÄFER, A. M., & ZIMMERMANN, H.-G.
- Venue
- International Journal of Neural Systems
- Publisher
- World Scientific Pub Co Pte Ltd
- Published
- 2007-08
- Volume
- 17
- Issue
- 04
- Pages
- 253-263
- ISSN
- 0129-0657 (ISSN-L)
- 1793-6462
- OpenAlex
- W2024450772
- Cited by
- 196
- References
- 3
- FWCI
- 7.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- SCHÄFER, A. M., & ZIMMERMANN, H.-G. (2007). RECURRENT NEURAL NETWORKS ARE UNIVERSAL APPROXIMATORS. International Journal of Neural Systems. https://doi.org/10.1142/s0129065707001111
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
