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6 - Self-similarity: dimensions

Published online by Cambridge University Press:  06 July 2010

Holger Kantz
Affiliation:
Max-Planck-Institut für Physik komplexer Systeme, Dresden
Thomas Schreiber
Affiliation:
Max-Planck-Institut für Physik komplexer Systeme, Dresden
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Summary

Attractor geometry and fractals

In the preceding chapter we discussed the dynamical side of chaos which manifests itself in the sensitive dependence of the evolution of a system on its initial conditions. This strange behaviour in time of a deterministically chaotic system has its counterpart in the geometry of the set in phase space formed by the (non-transient) trajectories of the system, the attractor.

Attractors of dissipative chaotic systems (the kind of systems we are interested in) generally have a very complicated geometry, which led people to call them strange. However, strange sets can also occur without dissipation in more general settings. As we have pointed out already in Chapter 3, a system described by autonomous differential equations (a flow) cannot be chaotic in less than three dimensions. With the same argument that trajectories are not allowed to intersect in a deterministic system we can conclude that not only the phase space but also the attractor of a chaotic flow must be more than two dimensional. However, slightly more than two dimensions is sufficient and the motion on a 2 + ∈ dimensional fractal can indeed be chaotic. As we will see, strange attractors with fractional dimensions are typical of chaotic systems. Map-like systems can of course show chaos with attractor dimensions less than two. Noninteger dimensions are assigned to geometrical objects which exhibit an unusual kind of self-similarity and which show structure on all length scales.

Example 6.1 (Self-similarity of the NMR laser attractor). Such self-similarity is demonstrated in Fig. 6.1 for an attractor reconstructed from the NMR laser time series, Appendix B.2.

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Publisher: Cambridge University Press
Print publication year: 2003

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  • Self-similarity: dimensions
  • Holger Kantz, Max-Planck-Institut für Physik komplexer Systeme, Dresden, Thomas Schreiber, Max-Planck-Institut für Physik komplexer Systeme, Dresden
  • Book: Nonlinear Time Series Analysis
  • Online publication: 06 July 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511755798.008
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  • Self-similarity: dimensions
  • Holger Kantz, Max-Planck-Institut für Physik komplexer Systeme, Dresden, Thomas Schreiber, Max-Planck-Institut für Physik komplexer Systeme, Dresden
  • Book: Nonlinear Time Series Analysis
  • Online publication: 06 July 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511755798.008
Available formats
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To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Self-similarity: dimensions
  • Holger Kantz, Max-Planck-Institut für Physik komplexer Systeme, Dresden, Thomas Schreiber, Max-Planck-Institut für Physik komplexer Systeme, Dresden
  • Book: Nonlinear Time Series Analysis
  • Online publication: 06 July 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511755798.008
Available formats
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