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Lyapunov Exponents |
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Lyapunov Exponents A measure of the dynamics of an attractor. Each dimension has a Lyapunov exponent. A positive exponent measures sensitive dependence on initial conditions, or how much our forecasts can diverge based upon different estimates of starting conditions. Another way to view Lyapunov exponents is the loss of predictive ability as we look forward into time. Strange Attractors are characterized by at least one positive exponent. A negative exponent measures how points converge towards one another. Point Attractors are characterized by all negative variables. See: Attractor, Limit Cycle, Point Attractor, Strange Attractor. How to thank TFD for its existence? Tell a friend about us, add a link to this page, add the site to iGoogle, or visit webmaster's page for free fun content. |
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Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. |
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