The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm
Abstract
This paper presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which 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 RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data.
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Citations
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298623 · Cited by 3,917
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
Yingwei, L., Sundararajan, N., & Saratchandran, P. (1997) · Neural Computation · doi:10.1162/neco.1997.9.2.461 · Cited by 507
Self-citationAn 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-citationSelf-constructing wavelet neural network algorithm for nonlinear control of large structures
Wang, N., & Adeli, H. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.01.018 · Cited by 103
Metadata
- Authors
- Yingwei, L., Sundararajan, N., & Saratchandran, P.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1998-03
- Volume
- 9
- Issue
- 2
- Pages
- 308-318
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2099244771
- Cited by
- 449
- References
- 2
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.661125
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