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The European Physical Journal Special Topics

, Volume 143, Issue 1, pp 261–264 | Cite as

Haar basis and nonlinear modeling of complex systems

  • P. García
  • A. Merlitti
Article

Abstract.

In this work we introduce a technique to perform nonlinear modeling of chaotic time series using the kernel method. The basic idea behind this method is to map the data into a high dimensional space via nonlinear mapping and do a linear regression in this space. Here we use a Haar wavelet-like kernel to achieve the task. This strategy, in contrast to Support Vector Machines technique, shows the conceptual simplicity of least mean square algoritm for linear regression but allows local nonlinear aproximation of the system evolution, with low computational cost.

Keywords

Support Vector Machine Nonlinear Modeling European Physical Journal Special Topic Venezuela High Dimensional Space 
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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Copyright information

© EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2007

Authors and Affiliations

  • P. García
    • 1
  • A. Merlitti
    • 2
  1. 1.Departamento de Física AplicadaFacultad de Ingeniería, Universidad Central de Venezuela A.P.CaracasVenezuela
  2. 2.Departamento de Matemática AplicadaFacultad de Ingeniería, Universidad Central de VenezuelaCaracasVenezuela

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