The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Universal Approximation Using Radial-Basis-Function Networks
Abstract
There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.
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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
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Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
Wavelet-based statistical signal processing using hidden Markov models
Crouse, M. S., Nowak, R. D., & Baraniuk, R. G. (1998) · IEEE Transactions on Signal Processing · doi:10.1109/78.668544 · Cited by 1,643
Adaptive Neural Control of Uncertain MIMO Nonlinear Systems
Ge, S. S., & Wang, C. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.826130 · Cited by 971
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On Interpretability of Artificial Neural Networks: A Survey
Fan, F.-L., Xiong, J., Li, M., & Wang, G. (2021) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2021.3066428 · Cited by 561
Metadata
- Authors
- Park, J., & Sandberg, I. W.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1991-06
- Volume
- 3
- Issue
- 2
- Pages
- 246-257
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2113442785
- Cited by
- 4,112
- References
- 2
- FWCI
- 24.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- Park, J., & Sandberg, I. W. (1991). Universal Approximation Using Radial-Basis-Function Networks. Neural Computation. https://doi.org/10.1162/neco.1991.3.2.246
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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