Combined Electromagnetism-Like Mechanism Optimization Algorithm and ROLS with D-Optimality Learning for RBF Networks

  • Fang Jia
  • Jun Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6329)


The paper proposed a new self-constructed radial basis function network designing method via a two-level learning hierarchy. Aiming at getting stronger generalization ability and robustness, an integrated algorithm which combines the regularized orthogonal least square with learning with D-optimality experimental design method was introduced at the lower level, while electromagnetism-like mechanism algorithm for global optimization was employed at the upper level to search the optimal combination of three important learning parameters, i.e., the radial basis function width, regularized parameter and D-optimality weight parameter. Through simulation results, the effectiveness of the proposed algorithm was verified.


radial basis function network two-level learning hierarchy electromagnetism-like mechanism algorithm generalization ability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, S., Hong, X., Harris, C.J.: Sparse Kernel Regression Modeling Using Combined Locally Regularized Orthogonal Least Squares and D-Optimality Experimental Design. IEEE Transactions on Automatic Control 48(6), 1029–1036 (2003)CrossRefGoogle Scholar
  2. 2.
    Wei, H.K., Ding, W.M., Song, W.Z., Xu, S.X.: Dynamic method for designing RBF neural networks. Control Theory and Applications 19(5), 673–680 (2002)zbMATHGoogle Scholar
  3. 3.
    Chen, S., Hong, X., Harris, C.J.: Probability Density Function Estimation Using Orthogonal Forward Regression. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida (2007)Google Scholar
  4. 4.
    Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Neural Networks. IEEE Trans. Neural Network 2(2), 302–309 (1991)CrossRefGoogle Scholar
  5. 5.
    Zhao, W.B., Yang, L.Y., Wang, L.M.: The Hybrid Structure Optimization Algorithms of Radial Basis Probabilistic Neural Networks. Acta Simulata Systematica Sinica 16(10), 2175–2184 (2004)Google Scholar
  6. 6.
    Chen, S., Chng, E.S., Alkadhimi, K.: Regularized Orthogonal Least Squares Algorithm for Constructing Radial Basis Function Networks. Int. J. Contr. 64(5), 829–837 (1996)CrossRefzbMATHGoogle Scholar
  7. 7.
    MacKay, D.J.C.: Bayesian interpolation. Neural Compute 4(3), 415–447 (1992)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hong, X., Harris, C.J.: Nonlinear Model Structure Design and Construction Using Orthogonal Least Squares and D-Optimality Design. IEEE Trans. Neural Networks 13, 1245–1250 (2002)CrossRefGoogle Scholar
  9. 9.
    Hong, X., Harris, C.J.: Experimental Design And Model Construction Algorithms For Radial Basis Function Networks. International Journal of Systems Science 34(14-15), 733–745 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Bi̇rbi̇l, Ş.İ., Fang, S.C.: A Multi-point Stochastic Search Method for Global Optimization. Journal of Global Optimization 25, 263–282 (2003)CrossRefGoogle Scholar
  11. 11.
    Chen, J.F., Ren, Z.W.: A New Two-level Learning Design Approach for Radial Basis Function Neural Network. Computer Simulation 26(6), 151–155 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fang Jia
    • 1
  • Jun Wu
    • 2
  1. 1.Department of Control Science and EngineeringZhejiang UniversityHangzhou, Zhe JiangChina
  2. 2.National Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and ControlZhejiang UniversityHangzhouChina

Personalised recommendations