[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Tackling the “curse of dimensionality” of radial basis functional neural networks using a genetic algorithm

  • Applications of Evolutionary Computation Evolutionary Computation in Machine Learning, Neural Networks, and Fuzzy Systems
  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

Included in the following conference series:

Abstract

Radial Basis Function (RBF) neural networks offer the possibility of faster gradient-based learning of neuron weights compared with Multi-Layer Perceptron (MLP) networks. This apparent advantage of RBF networks is bought at the expense of requiring a large number of hidden layer nodes, particularly in high dimensional spaces (the “curse of dimensionality”). This paper proposes a representation and associated genetic operators which are capable of evolving RBF networks with relatively small numbers of hidden layer nodes and good generalisation properties. The genetic operators employed also overcome the “competing conventions” problem, for RBF networks at least, which has been a reported stumbling block in the application of crossover operators in evolutionary learning of directly encoded neural network architectures.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Broomhead D.S. and Lowe D., Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355, 1988.

    MathSciNet  Google Scholar 

  2. Poggio T. and Girosi F., Networks for approximation and learning. Proceedings of the IEEE 78, 1481–1497, 1990.

    Article  Google Scholar 

  3. Haykin S. Neural Networks. Macmillan College Publishing Company, New York, NY, 1994.

    Google Scholar 

  4. Schaffer J.D., Whitley D. and Eschelman L.J., Combinations of genetic algorithms and neural networks: a survey of the state of the art. In Proceedings of the International Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN-92), IEEE, pp1–27, 1992.

    Google Scholar 

  5. Yao X., A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 8, pp539–567, 1993

    Google Scholar 

  6. Miller G.F., Todd P.M., Hegde S.U., Designing neural networks using genetic algorithms. Complex Systems 4, pp461–476, 1990.

    Google Scholar 

  7. Angeline P.J., Saunders G.M. and Pollack J.B., An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5, 1, pp54–65, January 1994.

    Article  Google Scholar 

  8. Whitehead B.A. and Choate T.D., Evolving space-filling curves to distribute radial basis functions over an input space. IEEE Transactions on Neural Networks, 5, 1, pp 15–23, January 1994.

    Article  Google Scholar 

  9. Chen S., Wu Y. and Alkadhimi K., A two-layer learning method for radial basis function networks using combined genetic and regularised OLS algorithms. In Proceedings of the 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp245–249, 1995.

    Google Scholar 

  10. Neruda R., Functional equivalence and genetic learning of RBF networks. In Pearson D.W., Steele N.C. and Albrecht R.F. (eds) Artificial Neural Nets and Genetic Algorithms, pp53–56, Springer-Verlag, 1995.

    Google Scholar 

  11. Hecht-Nielson R., On the algebraic structure of feed-forward network weight spaces. In Advanced Neural Computers, pp129–135, Elsevier, 1990

    Google Scholar 

  12. Jang J.R. and Sun C.T. (1993). Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks, 4,1. pp156–159.

    Article  Google Scholar 

  13. Carse B. and Fogarty T.C. (1994). A fuzzy classifier system using the Pittsburgh approach. In Davidor Y., Schwefel H.P. and Maenner (eds) PPSN III-Proceedings of the International Conference on Evolutionary Computation, pp260–269. Springer-Verlag Berlin Heidelberg.

    Google Scholar 

  14. Carse B., Fogarty T.C. and Munro A., Evolving fuzzy rule based controllers using genetic algorithms. To appear in Fuzzy Sets and Systems, 1996.

    Google Scholar 

  15. Mackey M.C. and Glass L., Oscillation and chaos in phsiological control systems, Science vol. 197, pp287–289, 1977.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Carse, B., Fogarty, T.C. (1996). Tackling the “curse of dimensionality” of radial basis functional neural networks using a genetic algorithm. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1034

Download citation

  • DOI: https://doi.org/10.1007/3-540-61723-X_1034

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics