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

Ch. Sanjeev Kumar Dash
  • Department of Computer Science and Engineering, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar, 751024, India
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 / Ajit Kumar Behera
  • Department of Computer Application, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar, 751024, India
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 / Satchidananda Dehuri
  • Department of Systems Engineering, Ajou University, San 5, Woncheondong, Yeongtong-gu, suwon 443-749, South Korea
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 / Sung-Bae Cho
  • Sung-Bae Cho, Soft Computing Laboratory, Department of Computer Science, Yonsei University, 134 Shinchondong, Sudaemoon-gu, Seoul 120-749, South Korea
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  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-05-02 | DOI: https://doi.org/10.1515/comp-2016-0005

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Abstract

Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

Keywords: neural network; radial basis function networks; multi-criterions optimization; learning; classification; clustering; approximation

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About the article

Received: 2014-10-12

Accepted: 2015-08-24

Published Online: 2016-05-02

Citation Information: Open Computer Science, Volume 6, Issue 1, ISSN (Online) 2299-1093, DOI: https://doi.org/10.1515/comp-2016-0005.

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©2016 Ch. Sanjeev Kumar Dash et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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