Analysis of Observed Chaotic Data

Regular Dynamics: Newton to Poincaré; 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: Cramér-Rao Bounds The Chaos Toolkit: Making 'Physics' out of Chaos