Advertisement

Genetic Optimizations for Radial Basis Function and General Regression Neural Networks

  • Gül Yazıcı
  • Övünç Polat
  • Tülay Yıldırım
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

The topology of a neural network has a significant importance on the network’s performance. Although this is well known, finding optimal configurations is still an open problem. This paper proposes a solution to this problem for Radial Basis Function (RBF) networks and General Regression Neural Network (GRNN) which is a kind of radial basis networks. In such networks, placement of centers has significant effect on the performance of network. The centers and widths of the hidden layer neuron basis functions are coded in a chromosome and these two critical parameters are determined by the optimization using genetic algorithms. Thyroid, iris and escherichia coli bacteria datasets are used to test the algorithm proposed in this study. The most important advantage of this algorithm is getting succesful results by using only a small part of a benchmark. Some numerical solution results indicate the applicability of the proposed approach.

Keywords

Genetic Algorithm Radial Basis Function Radial Basis Function Neural Network General Regression Neural Network Genetic Optimization 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Marco, N., Désidéri, J., Lanteri, S.: Multi Objective Optimization in CFD by Genetic Algorithms, Institut National De Recherce Informatique Et En Automatique (1999)Google Scholar
  2. 2.
    Barreto, A.M.S., et al.: Growing Compact RBF Networks Using a Genetic Algorithm. In: 7th Brazilian Symposium on Neural Networks, pp. 61–66 (2002)Google Scholar
  3. 3.
    de Lacerda, E.G.M., de Carvalho, A.C.P.L.F., Ludermir, T.B.: Evolutionary Optimization of RBF Networks. In: Sixth Brazilian Symposium, pp. 219–224 (2000)Google Scholar
  4. 4.
    Zuo, G., Liu, W., Ruan, X.: Genetic Algorithm Based RBF Neural Network for Voice Conversion. In: Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou. P.R. China, June 15-19 (2004)Google Scholar
  5. 5.
    Burdsall, B., Carrier, C.G.: GA-RBF: A Self-Optimising RBF Network. In: Proceedings of the Third International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 1997), pp. 348–351. Springer, Heidelberg (1997)Google Scholar
  6. 6.
    Specht, D.F.: A general regression neural network. IEEE Trans. Neural Networks 2, 568–576 (1991)CrossRefGoogle Scholar
  7. 7.
    Specht, D.F.: Enhancements to probabilistic neural network. In: Proc. Int. Joint Conf. Neural Network, vol. 1, pp. 761–768 (1991)Google Scholar
  8. 8.
    Heimes, F., van Heuveln, B.: The normalized radial basis function neural network. In: IEEE International Conference on Systems, Man, and Cybernetics, October 11-14, 1998, vol. 2, pp. 1609–1614 (1998)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic algorithm in search, optimization, and machine learning. Addison-Wesley, Reading (1989)Google Scholar
  10. 10.
    Zhang, Q., He, X., Liu, J.: RBF Network Based On Genetic Algorithm Optimization For Nonlinear Time Series Prediction. In: ISCAS 2003 Proceedings of the 2003 International Symposium on Circuits and Systems, vol. 5, pp. 693–696 (2003)Google Scholar
  11. 11.
    Hatanaka, T., Kondo, N., Uosaki, K.: Multi-Objective Structure Selection for Radial Basis Function Networks Based on Genetic Algorithm. In: The 2003 Congress on Evolutionary Computation CEC 2003, vol. 2, pp. 1095–1100 (2003)Google Scholar
  12. 12.
    Avci, M., Yıldırım, T.: Classification of Escherichia Coli Bacteria by Artificial Neural Networks. In: Proc. of the IEEE International Symposium on Intelligent Systems, Varna, Bulgaria, vol. 3, pp. 16–20 (2002)Google Scholar
  13. 13.
    Bolat, B., Yıldırım, T.: A Data Selection Method for Probalistic Neural Networks. Journal of Electrical & Electronic Engineering, Istanbul 4(2) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gül Yazıcı
    • 1
  • Övünç Polat
    • 2
  • Tülay Yıldırım
    • 2
  1. 1.Beko ElektronicIstanbulTurkey
  2. 2.Electronics and Communications Engineering DepartmentYıldız Technical UniversityIstanbulTurkey

Personalised recommendations