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Local-Global Neural Networks for Interpolation

  • Carlos E. Pedreira
  • Luiz Carlos Pedroza
  • Mayte Fariñas
Conference paper

Abstract

In this paper a new connectionist model is proposed. The proposed architecture is trained by a scheme based on partition of the function domain, approximating the generator function by a set of very simple supporting functions. This method has an interesting ability concerning interpolation. A synthetic experiment and areal data missing data application are presented.

Keywords

Membership Function Simple Function Supporting Function Electricity Load Function Domain 
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]
    Haykin S. “Neural Networks — A Comprehensive Foundation”, Prentice Hall, second edition, 1999.Google Scholar
  2. [2]
    Pedroza L.C Pedreira C.E. “Multilayer Neural Networks and Function Reconstruction by Using a priori Knowledge” International Journal of Neural Systems, Volume 9, number 3, pp 251–256, 1999.CrossRefGoogle Scholar
  3. [3]
    Bartle, R.G. Elements of integration. Wiley. New York, 1966Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Carlos E. Pedreira
    • 1
  • Luiz Carlos Pedroza
    • 2
  • Mayte Fariñas
    • 1
  1. 1.Catholic University, PUC-RIORio de JaneiroBrazil
  2. 2.CEFET-RJRio de JaneiroBrazil

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