A RBF neural network learning algorithm based on NCPSO

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A RBF (Radial Basis Function) neural network learning algorithm based on NCPSO (Niching Chaotic Mutation Particle Swarm Optimization) is proposed. Because of the niching method and chaotic mutation, NCPSO can be used to optimize the output weights of the RBF Neural Network. Niching method is introduced to improve the ability of global optimization. Chaotic mutation is mentioned to improve the solution. Compared with the RBF neural network learning algorithm based on the GA (Genetic Algorithm), simulation shows that the RBF neural network learning algorithm based on NCPSO mentioned in this paper has a lower error of tracking and higher speed.

Original languageEnglish
Title of host publicationProceedings of the 32nd Chinese Control Conference, CCC 2013
PublisherIEEE Computer Society
Pages3294-3299
Number of pages6
ISBN (Print)9789881563835
StatePublished - 18 Oct 2013
Externally publishedYes
Event32nd Chinese Control Conference, CCC 2013 - Xi'an, China
Duration: 26 Jul 201328 Jul 2013

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference32nd Chinese Control Conference, CCC 2013
Country/TerritoryChina
CityXi'an
Period26/07/1328/07/13

Keywords

  • Evolutionary Computational Techniques
  • Genetic Algorithm
  • Niching Chaotic Mutation Particle Swarm Optimization
  • Radial Basis Function Neural Network

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