Strange Attractors

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  • 05/27/09 Entire presentation available on WWW
  • Strange Attractors

    1. 1. Strange Attractors From Art to Science J. C. Sprott Department of Physics University of Wisconsin - Madison Presented at the Santa Fe Institute On June 20, 2000
    2. 2. Outline <ul><li>Modeling of chaotic data </li></ul><ul><li>Probability of chaos </li></ul><ul><li>Examples of strange attractors </li></ul><ul><li>Properties of strange attractors </li></ul><ul><li>Attractor dimension scaling </li></ul><ul><li>Lyapunov exponent scaling </li></ul><ul><li>Aesthetics </li></ul><ul><li>Simplest chaotic flows </li></ul><ul><li>New chaotic electrical circuits </li></ul>
    3. 3. Typical Experimental Data Time 0 500 x 5 -5
    4. 4. General 2-D Iterated Quadratic Map <ul><li>x n +1 = a 1 + a 2 x n + a 3 x n 2 + a 4 x n y n + a 5 y n + a 6 y n 2 </li></ul><ul><li>y n +1 = a 7 + a 8 x n + a 9 x n 2 + a 10 x n y n + a 11 y n + a 12 y n 2 </li></ul>
    5. 5. Solutions Are Seldom Chaotic Chaotic Data (Lorenz equations) Solution of model equations Chaotic Data (Lorenz equations) Solution of model equations Time 0 200 x 20 -20
    6. 6. How common is chaos? Logistic Map x n +1 = Ax n (1 - x n ) -2 4 A Lyapunov Exponent 1 -1
    7. 7. A 2-D Example (Hénon Map) 2 b -2 a -4 1 x n +1 = 1 + ax n 2 + bx n -1
    8. 8. General 2-D Quadratic Map 100 % 10% 1% 0.1% Bounded solutions Chaotic solutions 0.1 1.0 10 a max
    9. 9. Probability of Chaotic Solutions Iterated maps Continuous flows (ODEs) 100% 10% 1% 0.1% 1 10 Dimension
    10. 10. Neural Net Architecture tanh
    11. 11. % Chaotic in Neural Networks
    12. 12. Types of Attractors Fixed Point Limit Cycle Torus Strange Attractor Spiral Radial
    13. 13. Strange Attractors <ul><li>Limit set as t   </li></ul><ul><li>Set of measure zero </li></ul><ul><li>Basin of attraction </li></ul><ul><li>Fractal structure </li></ul><ul><ul><li>non-integer dimension </li></ul></ul><ul><ul><li>self-similarity </li></ul></ul><ul><ul><li>infinite detail </li></ul></ul><ul><li>Chaotic dynamics </li></ul><ul><ul><li>sensitivity to initial conditions </li></ul></ul><ul><ul><li>topological transitivity </li></ul></ul><ul><ul><li>dense periodic orbits </li></ul></ul><ul><li>Aesthetic appeal </li></ul>
    14. 14. Stretching and Folding
    15. 15. Correlation Dimension 5 0.5 1 10 System Dimension Correlation Dimension
    16. 16. Lyapunov Exponent 1 10 System Dimension Lyapunov Exponent 10 1 0.1 0.01
    17. 17. Aesthetic Evaluation
    18. 18. Sprott (1997) <ul><li>d x /d t = y </li></ul><ul><li>d y /d t = z </li></ul><ul><li>d z /d t = - az + y 2 - x </li></ul><ul><li>5 terms, 1 quadratic nonlinearity, 1 parameter </li></ul>“ Simplest Dissipative Chaotic Flow”
    19. 19. Linz and Sprott (1999) <ul><li>d x /d t = y </li></ul><ul><li>d y /d t = z </li></ul><ul><li>d z /d t = - az - y + | x | - 1 </li></ul><ul><li>6 terms, 1 abs nonlinearity, 2 parameters (but one =1) </li></ul>
    20. 20. First Circuit
    21. 21. Bifurcation Diagram for First Circuit
    22. 22. Second Circuit
    23. 23. Third Circuit
    24. 24. Chaos Circuit
    25. 25. Summary <ul><li>Chaos is the exception at low D </li></ul><ul><li>Chaos is the rule at high D </li></ul><ul><li>Attractor dimension ~ D 1/2 </li></ul><ul><li>Lyapunov exponent decreases with increasing D </li></ul><ul><li>New simple chaotic flows have been discovered </li></ul><ul><li>New chaotic circuits have been developed </li></ul>
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