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Author:

Meng, Xi (Meng, Xi.) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (Scholars:乔俊飞) | Han, Hong-Gui (Han, Hong-Gui.) (Scholars:韩红桂)

Indexed by:

EI Scopus PKU CSCD

Abstract:

For the problem that it is difficult to determine the hidden layer structure of the radial basis function(RBF) neural network, based on the good online classified characteristic of adaptive resonance theory(ART) neural network, a self-organizing RBF neural network structure design algorithm is proposed. The algorithm uses the clustering characteristic of ART neural network to design the RBF neural network structure. Through the similarity comparison of input vector, the number of the hidden layer nodes and initial parameters are determined, so that the network has simplified structure. The experiment results show that the proposed structure has a smaller number of nodes, fast learning speed and better approximation ability.

Keyword:

Functions Multilayer neural networks Arts computing Clustering algorithms Radial basis function networks

Author Community:

  • [ 1 ] [Meng, Xi]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Qiao, Jun-Fei]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Han, Hong-Gui]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address:

  • 乔俊飞

    [qiao, jun-fei]college of electronic information and control engineering, beijing university of technology, beijing; 100124, china

Email:

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Source :

Control and Decision

ISSN: 1001-0920

Year: 2014

Issue: 10

Volume: 29

Page: 1876-1880

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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