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Continuation of chaotic fields by RBFNN

  • Complex Systems Dynamics
  • Conference paper
  • First Online:
Biological and Artificial Computation: From Neuroscience to Technology (IWANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

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Abstract

A chaotic field generator is represented by a non-linear equation. Its generating function is modeled empirically by a statistical non-parametric estimator. The estimator corresponds to a radial basis function neural network which learns from a record of a field given in some initial domain to predict the field distribution elsewhere. The performance of the generator is demonstrated by prediction of a chaotic series and a regular as well as a chaotic surface.

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References

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Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering, University of Ljubljana, pob 394, SI-1001, Ljubljana, Slovenia

    Igor Grabec & Simon Mandelj

Authors
  1. Igor Grabec
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  2. Simon Mandelj
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Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

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© 1997 Springer-Verlag Berlin Heidelberg

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Grabec, I., Mandelj, S. (1997). Continuation of chaotic fields by RBFNN. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032519

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  • DOI: https://doi.org/10.1007/BFb0032519

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

  • eBook Packages: Springer Book Archive

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