The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
Abstract
A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation. The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method. Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C/ radicaln), where n is number of basis functions and with C independent of n. Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions. Some possible ways of enhancing the accuracy of multivariate function approximations are discussed.
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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
Chen, C. L. P., & Liu, Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2716952 · Cited by 1,957
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,914
Universal Approximation Capability of Broad Learning System and Its Structural Variations
Chen, C. L. P., Liu, Z., & Feng, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2866622 · Cited by 549
A review on neural networks with random weights
Cao, W., Wang, X., Ming, Z., & Gao, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.040 · Cited by 466
GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition
Zhang, T., Wang, X., Xu, X., & Chen, C. L. P. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2937768 · Cited by 396
Metadata
- Authors
- Igelnik, B., & Pao, Y.-H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1995
- Volume
- 6
- Issue
- 6
- Pages
- 1320-1329
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W1986278072
- Cited by
- 955
- References
- 3
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- closed
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