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A Novel Elliptical Basis Function Neural Networks Optimized by Particle Swarm Optimization

  • Ji-Xiang Du
  • Chuan-Min Zhai
  • Zeng-Fu Wang
  • Guo-Jun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this paper, a novel model of elliptical basis function neural networks (EBFNN) is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. Finally, the experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.

Keywords

Particle Swarm Optimization Radial Basis Function Neural Network Hybrid Particle Swarm Optimization Good Generalization Performance Radial Basis Function Neural Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)Google Scholar
  2. 2.
    Kositsky, M., Ullman, S.: Learning Class Regions by the Union of Ellipsoids. In: Proceedings of the 13th International Conference on Pattern Recognition, pp. 750–757 (1996)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C.: A Discrete Binary Version of the Particle Swarm Algorithm. In: Proceedings of IEEE Int’l Conference on Systems, Man, and Cybernetics, pp. 4104–4108 (1997)Google Scholar
  4. 4.
    Zhang, G.J., Wang, X.F., Huang, D.S., Chi, Z., Cheung, Y.M., Du, J.X., Wan, Y.Y.: A Hypersphere Method for Plant Leaves Classification. In: Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video & Speech Processing (ISIMP 2004), Hong Kong, China, pp. 165–168 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ji-Xiang Du
    • 1
    • 2
  • Chuan-Min Zhai
    • 3
  • Zeng-Fu Wang
    • 1
  • Guo-Jun Zhang
    • 2
  1. 1.Department of AutomationUniversity of Science and Technology of China 
  2. 2.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  3. 3.Department of Mechanical EngineeringHefei University 

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