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Analysis of Observed Chaotic Data

Abarbanel, Henry D. I., Gollub, Jerry P.·1996·Physics Today

Abstract

Regular Dynamics: Newton to Poincare KAM Theorem | Bifurcations: Routes to Chaos, Stability and Instability | Reconstruction of Phase Space: Regular and Chaotic Motions Observed Chaos | Choosing Time Delays: Chaos as an Information Source Average Mutual Information. | Choosing the Dimension of Reconstructed Phase Space | Invariants of the Motion: Global & Local Lyapunov Exponents Lorenz Model | Modeling Chaos: Local & Global Models Phase Space Models | Signal Separation: Probabilistic Cleaning 'Blind' Signal Separation | Control and Chaos: Parametric Control Examples of Control (including magnetoelastic ribbon, electric circuits, cardiac tissue) | Synchronization of Chaotic Systems: Identical or Dissimilar Systems Chaotic Nonlinear Circuits | Other Example Systems: Laser Intensity Fluctuations Volume Fluctuations of the Great Salt Lake Motion in a Fluid Boundary Layer | Estimating in Chaos: Cramer-Rao Bounds | The Chaos Toolkit: Making 'Physics' out of Chaos

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Metadata

Authors
Abarbanel, H. D. I., & Gollub, J. P.
Venue
Physics Today
Published
1996-11-01
Volume
49
Issue
11
Pages
86-88
ISSN
  • 0031-9228 (ISSN-L)
  • 1945-0699
OpenAlex
W2068844879
Cited by
1,821
References
0
FWCI
35.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Analysis of Observed Chaotic Data. https://app.cochat.ai/papers/10.1063/1.881528
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Abarbanel, H. D. I., & Gollub, J. P. (1996). Analysis of Observed Chaotic Data. Physics Today. https://doi.org/10.1063/1.881528

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