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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4131 LNCS - I, Issue , 2006, Pages 632-640

Recurrent neural networks are universal approximators

(2) Schäfer, Anton Maximilian a,b Zimmermann, Hans Georg a

a SIEMENS AG (Germany)

b UNIVERSITY OF ULM (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; DYNAMICS; FUNCTIONS; MATHEMATICAL MODELS; STATE SPACE METHODS;

DYNAMICAL SYSTEMS; OPEN DYNAMICAL SYSTEMS; STATE SPACE MODEL;

FEEDFORWARD NEURAL NETWORKS;

EID: 33749820013 PISSN: 03029743 EISSN: 16113349 Source Type: Book Series
DOI: 10.1007/11840817_66 Document Type: Conference Paper

Times cited : (264)

References (8)

1
- 0024880831
- Multi-layer feedforward networks are universal approximators
- Hornik, K., Stinchcombe, M., White, H.: Multi-layer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-366
- (1989) Neural Networks , vol.2 , pp. 359-366
- Hornik, K.¹ Stinchcombe, M.² White, H.³

2
- 0024861871
- Approximation by superpositions of a sigmoidal function
- Springer, New York
- Cybenko, G.: Approximation by superpositions of a sigmoidal function. In: Mathematics of Control, Signals and Systems. Springer, New York (1989) 303-314
- (1989) Mathematics of Control, Signals and Systems , pp. 303-314
- Cybenko, G.¹

3
- 0024866495
- On the approximate realization of continuous mappings by neural networks
- Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183-192
- (1989) Neural Networks , vol.2 , pp. 183-192
- Funahashi, K.I.¹

4
- 33749841590
- Identification and forecasting of large dynamical systems by dynamical consistent neural networks
- Haykin, S., J. Principe, T.S., McWhirter, J., eds. MIT Press
- Zimmermann, H.G., Grothmann, R., Schaefer, A.M., Tictz, C.: Identification and forecasting of large dynamical systems by dynamical consistent neural networks. In Haykin, S., J. Principe, T.S., McWhirter, J., eds.: New Directions in Statistical Signal Processing: From Systems to Brain. MIT Press (2006)
- (2006) New Directions in Statistical Signal Processing: From Systems to Brain
- Zimmermann, H.G.¹ Grothmann, R.² Schaefer, A.M.³ Tictz, C.⁴

5
- 0003413187
- Macmillan, New York
- Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)
- (1994) Neural Networks: A Comprehensive Foundation
- Haykin, S.¹

6
- 2942684766
- Neural network architectures for the modeling of dynamical systems
- Kolen, J.F., Kremer, S., eds, IEEE Press
- Zimmermann, H.G., Neuneier, R.: Neural network architectures for the modeling of dynamical systems. In Kolen, J.F., Kremer, S., eds.: A Field Guide to Dynamical Recurrent Networks, IEEE Press (2001) 311-350
- (2001) A Field Guide to Dynamical Recurrent Networks , pp. 311-350
- Zimmermann, H.G.¹ Neuneier, R.²

7
- 0000646059
- Learning internal representations by error propagation
- Rumelhart, D.E., et al., J.L.M., eds. MIT Press, Cambridge
- Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In Rumelhart, D.E., et al., J.L.M., eds.: Parallel Distributed Processing: Explorations in The Microstructure of Cognition. Volume 1. MIT Press, Cambridge (1986) 318-362
- (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition , vol.1 , pp. 318-362
- Rumelhart, D.E.¹ Hinton, G.E.² Williams, R.J.³

8
- 33749860675
- On the approximation capability of recurrent neural networks
- Hammer, B.: On the approximation capability of recurrent neural networks. In: International Symposium on Neural Computation. (1998)
- (1998) International Symposium on Neural Computation
- Hammer, B.¹

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