The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results
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Citations
Cites
Handbook of Mathematical Functions
McQuarrie, D. A. (1966) · American Journal of Physics · doi:10.1119/1.1972842 · Cited by 40,433
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
Universal Approximation Using Radial-Basis-Function Networks
Park, J., & Sandberg, I. W. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.246 · Cited by 4,112
Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R. (1993) · IEEE Transactions on Information Theory · doi:10.1109/18.256500 · Cited by 2,804
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Widrow, B., & Lehr, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58323 · Cited by 2,242
A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)
Albus, J. S. (1975) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3426922 · Cited by 2,177 · cited 1×
“Observe that with this approach we form a partition of the domain similar to the one used by CMAC (Albus, 1975).”
MethodLeshno, M., Lin, V. Y., Pinkus, A., & Schocken, S. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80131-5 · Cited by 2,121
Approximation and Radial-Basis-Function Networks
Park, J., & Sandberg, I. W. (1993) · Neural Computation · doi:10.1162/neco.1993.5.2.305 · Cited by 816
Kolmogorov's theorem and multilayer neural networks
Kůrková, V. (1992) · Neural Networks · doi:10.1016/0893-6080(92)90012-8 · Cited by 747
Cited by
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Self-citationApproximation theory of the MLP model in neural networks
Pinkus, A. (1999) · Acta Numerica · doi:10.1017/s0962492900002919 · Cited by 1,529
Sun, L., Gao, H., Pan, S., & Wang, J.-X. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.112732 · Cited by 1,045
Constructing high‐dimensional neural network potentials: A tutorial review
Behler, J. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24890 · Cited by 884
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Metadata
- Authors
- Scarselli, F., & Chung Tsoi, A.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 1998-01
- Volume
- 11
- Issue
- 1
- Pages
- 15-37
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W1982029046
- Cited by
- 571
- References
- 22
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results. https://app.cochat.ai/papers/10.1016/s0893-6080(97)00097-x
- The paper
- Scarselli, F., & Chung Tsoi, A. (1998). Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results. Neural Networks. https://doi.org/10.1016/s0893-6080(97)00097-x
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
