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

Ch Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung Bae Cho

Research output: Contribution to journalReview article

13 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)33-63
Number of pages31
JournalOpen Computer Science
Volume6
Issue number1
DOIs
Publication statusPublished - 2016 Jan 1

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Radial basis function networks
Neural networks
Tuning

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Dash, Ch Sanjeev Kumar ; Behera, Ajit Kumar ; Dehuri, Satchidananda ; Cho, Sung Bae. / Radial basis function neural networks : A topical state-of-the-art survey. In: Open Computer Science. 2016 ; Vol. 6, No. 1. pp. 33-63.
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Radial basis function neural networks : A topical state-of-the-art survey. / Dash, Ch Sanjeev Kumar; Behera, Ajit Kumar; Dehuri, Satchidananda; Cho, Sung Bae.

In: Open Computer Science, Vol. 6, No. 1, 01.01.2016, p. 33-63.

Research output: Contribution to journalReview article

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