The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks
Abstract
This article presents a sequential learning algorithm for function approximation and time-series prediction using a minimal radial basis function neural network (RBFNN). The algorithm combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RBFNN. The performance of the algorithm is compared with RAN and the enhanced RAN algorithm of Kadirkamanathan and Niranjan (1993) for the following benchmark problems: (1) hearta from the benchmark problems database PROBEN1, (2) Hermite polynomial, and (3) Mackey-Glass chaotic time series. For these problems, the proposed algorithm is shown to realize RBFNNs with far fewer hidden neurons with better or same accuracy.
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Citations
Cites
Fast Learning in Networks of Locally-Tuned Processing Units
Moody, J., & Darken, C. J. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.281 · Cited by 4,194
A Resource-Allocating Network for Function Interpolation
Platt, J. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.213 · Cited by 1,354
On the training of radial basis function classifiers
Musavi, M. T., Ahmed, W., Chan, K. H., Faris, K. B., & Hummels, D. M. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80038-3 · Cited by 569
A Function Estimation Approach to Sequential Learning with Neural Networks
Kadirkamanathan, V., & Niranjan, M. (1993) · Neural Computation · doi:10.1162/neco.1993.5.6.954 · Cited by 458
Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.661125 · Cited by 449
Self-citationAn efficient MDL-based construction of RBF networks
Leonardis, A., & Bischof, H. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00051-3 · Cited by 106
Bayesian Radial Basis Functions of Variable Dimension
Holmes, C. C., & Mallick, B. K. (1998) · Neural Computation · doi:10.1162/089976698300017421 · Cited by 93
Cited by
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C.-K. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614
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
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781
An Efficient Sequential Learning Algorithm for Growing and Pruning RBF (GAP-RBF) Networks
Huang, G.-B., Saratchandran, P., & Sundararajan, N. (2004) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2004.834428 · Cited by 391
Self-citationKaveh, M., & Mesgari, M. S. (2023) · Neural Processing Letters · doi:10.1007/s11063-022-11055-6 · Cited by 292
Metadata
- Authors
- Yingwei, L., Sundararajan, N., & Saratchandran, P.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1997-02-01
- Volume
- 9
- Issue
- 2
- Pages
- 461-478
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W1984049995
- Cited by
- 507
- References
- 7
- FWCI
- 11.89
- Type
- journal article
- Language
- en
- Open access
- closed
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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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