The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Orthogonal least squares learning algorithm for radial basis function networks
Abstract
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications.
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High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
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Metadata
- Authors
- Chen, S., Cowan, C. F. N., & Grant, P. M.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1991-03
- Volume
- 2
- Issue
- 2
- Pages
- 302-309
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2155399784
- Cited by
- 3,362
- References
- 23
- FWCI
- 23.85
- Type
- journal article
- Language
- en
- Open access
- closed
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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).
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