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Optimal design of radial basis function using Taguchi method

  • Ho Kim Eun*
  • , Hak Hyun Kyung
  • , Keun Kwak Yoon
  • *Corresponding author for this work
  • Korea Advanced Institute of Science and Technology

Research output: Contribution to conferenceConference paperpeer-review

Abstract

Development of the radial basis function networks (RBFNs) can be divided into two stages. First, learning the centres and widths of the radial basis function and next, learning the connection weight. The performance of the RBFN depends entirely on these two learning algorithms. Hence, in this paper, we proposed a new algorithm wherein the centres and widths of the radial basis function in regression problem are selected using the Taguchi method. Some experiments of function estimation are conducted in order to illustrate the performance of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Pages171-177
Number of pages7
StatePublished - 2005
Event2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 - Beijing, China
Duration: 2005.10.132005.10.15

Publication series

NameProceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Volume1

Conference

Conference2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Country/TerritoryChina
CityBeijing
Period05.10.1305.10.15

Keywords

  • Centres and widths selection
  • Radial basis function networks
  • Regression
  • Taguchi method

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