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Radial basis function neural networks: a topical state-of-the-art survey

Radial basis function neural networks: a topical state-of-the-art survey AbstractRadial basis function networks (RBFNs) havegained widespread appeal amongst researchers and haveshown good performance in a variety of application domains.They have potential for hybridization and demonstratesome interesting emergent behaviors. This paperaims to offer a compendious and sensible survey on RBFnetworks. The advantages they offer, such as fast trainingand global approximation capability with local responses,are attracting many researchers to use them in diversifiedfields. The overall algorithmic development of RBF networksby giving special focus on their learning methods,novel kernels, and fine tuning of kernel parameters havebeen discussed. In addition, we have considered the recentresearch work on optimization of multi-criterions inRBF networks and a range of indicative application areasalong with some open source RBFN tools. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Open Computer Science de Gruyter

Radial basis function neural networks: a topical state-of-the-art survey

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Publisher
de Gruyter
Copyright
© 2016 Ch. Sanjeev Kumar Dash et al.
eISSN
2299-1093
DOI
10.1515/comp-2016-0005
Publisher site
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Abstract

AbstractRadial basis function networks (RBFNs) havegained widespread appeal amongst researchers and haveshown good performance in a variety of application domains.They have potential for hybridization and demonstratesome interesting emergent behaviors. This paperaims to offer a compendious and sensible survey on RBFnetworks. The advantages they offer, such as fast trainingand global approximation capability with local responses,are attracting many researchers to use them in diversifiedfields. The overall algorithmic development of RBF networksby giving special focus on their learning methods,novel kernels, and fine tuning of kernel parameters havebeen discussed. In addition, we have considered the recentresearch work on optimization of multi-criterions inRBF networks and a range of indicative application areasalong with some open source RBFN tools.

Journal

Open Computer Science – de Gruyter

Published: Jan 1, 2016

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