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  2. Lyapunov time - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_time

    The Lyapunov time mirrors the limits of the predictability of the system. By convention, it is defined as the time for the distance between nearby trajectories of the system to increase by a factor of e. However, measures in terms of 2-foldings and 10-foldings are sometimes found, since they correspond to the loss of one bit of information or ...

  3. Lyapunov exponent - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_exponent

    There are no inherent limitations on the number of variables, parameters etc. Lyap which includes source code written in Fortran, can also calculate the Lyapunov direction vectors and can characterize the singularity of the attractor, which is the main reason for difficulties in calculating the more negative exponents from time series data.

  4. Lyapunov equation - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_equation

    In particular, the discrete-time Lyapunov equation (also known as Stein equation) for is A X A H − X + Q = 0 {\displaystyle AXA^{H}-X+Q=0} where Q {\displaystyle Q} is a Hermitian matrix and A H {\displaystyle A^{H}} is the conjugate transpose of A {\displaystyle A} , while the continuous-time Lyapunov equation is

  5. Lyapunov dimension - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_dimension

    The exact limit values of finite-time Lyapunov exponents, if they exist and are the same for all , are called the absolute ones [3] {+ (,)} = {()} {} and used in the Kaplan–Yorke formula. Examples of the rigorous use of the ergodic theory for the computation of the Lyapunov exponents and dimension can be found in. [ 11 ] [ 12 ] [ 13 ]

  6. Controllability Gramian - Wikipedia

    en.wikipedia.org/wiki/Controllability_Gramian

    Linear Time Invariant (LTI) Systems are those systems in which the parameters , , and are invariant with respect to time. One can observe if the LTI system is or is not controllable simply by looking at the pair ( A , B ) {\displaystyle ({\boldsymbol {A}},{\boldsymbol {B}})} .

  7. Lyapunov theorem - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_theorem

    Lyapunov fractal, bifurcational fractals derived from an extension of the logistic map in which the degree of the growth of the population periodically switches between two values; Lyapunov time, characteristic timescale on which a dynamical system is chaotic; Probability theory, the branch of mathematics concerned with probability

  8. Oseledets theorem - Wikipedia

    en.wikipedia.org/wiki/Oseledets_theorem

    The values of the Lyapunov exponents are invariant with respect to a wide range of coordinate transformations. Suppose that g : X → X is a one-to-one map such that ∂ g / ∂ x {\displaystyle \partial g/\partial x} and its inverse exist; then the values of the Lyapunov exponents do not change.

  9. Lyapunov function - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_function

    A Lyapunov function for an autonomous dynamical system {: ˙ = ()with an equilibrium point at = is a scalar function: that is continuous, has continuous first derivatives, is strictly positive for , and for which the time derivative ˙ = is non positive (these conditions are required on some region containing the origin).