Given this new measure, let's apply it to the logistic equation and see if it works. conditions (within the basin of attraction) and perturbation Negative Lyapunov exponents are characteristic of, The orbit is a neutral fixed point (or an eventually fixed point). For the Bakers’ map, the Lyapunov exponents can be calculated analytically. The Lyapunov characteristic exponent (LCE) is associ-ated with the asymptotic dynamic stability of the system: it is a measure of the exponential divergence of trajecto-ries in phase space. code for calculating the The third chapter deals with some of the definitions and applications of the word dimension. at each iteration. So similar and yet so alike. A physical system with this exponent is. It would be nice to have a simple measure that could discriminate among the types of orbits in the same manner as the parameters of the harmonic oscillator. Generally the calculation of Lyapunov exponents, as defined above, cannot be carried out analytically, and in most cases one must resort to numerical techniques. Sometimes you can get the whole spectrum of exponents using the Then compare the result with that obtained when r = 3. The results are listed in the table below and agree with the orbits. Substitute some number into it. example for the Lorenz attractor is available. This does not preclude any organization as a pattern may emerge. Where, if I understand things correctly, f ′ ( x i) is the derivative of f … For a discrete system, the orbits will look like snow on a television set. The logistic equation is unruly. above method, for example when the system is a two dimensional For a continuous system, the phase space would be a tangled sea of wavy lines like a pot of spaghetti. so as to avoid the all too common mistake of quoting more digits A Lyapunov exponent of zero indicates that the system is in some sort of steady state mode. So for this, define d( k )>, where is averaging over all starting pairs t i , t j , such that the initial distance d (0) = | t i – t j | is less than some fixed small value. Well, I tried those numbers in the equation, but I kept getting an error message from r = 2. largest Lyapunov exponent. The closed loops correspond to stable regions with fixed points or fixed periodic points at their centers. A physical example can be found in Brownian motion. Take any arbitrarily small volume in the phase space of a chaotic system. End of diversion. If a system is unstable, like pins balanced on their points, then the orbits diverge exponentially for a while, but eventually settle down. This is actually the location of the first bifurcation. The usual test for chaos is calculation of the line of separation. It works for discrete as well as continuous systems. Despite their peculiar behavior, chaotic systems are conservative. nearby orbits and to calculate their average logarithmic rate of The answer lies in the many definitions of dimension. By convention, the natural logarithm (base- e) is usually used, but for maps, the Lyapunov exponent is often quoted in bits per iteration, in which case you would need to use base-2. Will the volume send forth connected pseudopodia and evolve like an amoeba, atomize like the liquid ejected from a perfume bottle, or foam up like a piece of Swiss cheese and grow ever more porous? You can see there was some disagreement in the sources as to exactly where the chaotic regime begins. When one has access to the To estimate the uncertainty in your calculated Lyapunov exponent, do. At this "r" value the system quickly settles on to the fixed point of ½, which makes. Thus the snow may be a bit lumpy. When one only has access to an experimental data record, Lexp - Lyapunov exponents to each time value. The logistic equation is superstable at this point, which makes the Lyapunov exponent equal to negative infinity (the limit of the log function as the variable approaches zero). Volume is preserved, but shape is not. LYAPUNOV EXPONENTS 119 Figure 6.2: A long-time numerical calculation of the leading Lyapunov exponent requires rescaling the dis-tance in order to keep the nearby trajectory separation within the linearized flow range. The orbit is unstable and chaotic. If we use one of the orbits a reference orbit, then the separation between the two orbits will also be a function of time. Whenever they get too far apart, one of the rate of state space contraction averaged along the orbit (the to the sensitive dependence on initial conditions. A conservative procedure is to do this For chaotic points, the function Δx(X0, t) will behave erratically. This chapter describes the methods for constructing some of them. The logistic equation is a discrete, second-order, difference equation used to model animal populations. Analysis. The first chapter introduces the basics of one-dimensional iterated maps. This number, called the Lyapunov exponent "λ" [lambda], is useful for distinguishing among the various types of orbits. Nearby points, no matter how close, will diverge to any arbitrary separation. In a system with attracting fixed points or attracting periodic points, Δx(X0, t) diminishes asymptotically with time. A fractal is a geometric pattern exhibiting an infinite level of repeating, self-similar detail that can't be described with classical geometry. directions. Note also that because the calculator can only approximate the value of 1 + âˆš5, the Lyapunov exponent for the superstable 2‑cycle is only a relatively large negative number and not negative infinity as expected. The paramenters of the system determine what it does. must be zero for a continuous flow. be changed slightly since orbits quickly become uncorrelated due The first number should be negative, indicating a stable system, and the second number should be positive, a warning of chaos (Dewdney). See also the code for calculating the No calculator can find the logarithm of zero and so the program fails. This leads to the creation of mathematical monsters called fractals. You may get run-time errors when evaluating the logarithm if d1 becomes so small as to be indistinguishable from zero. the same except that the resulting exponent is divided by the The exponent provides a means of ascertaining whether the behavior of a system is chaotic. Standard map orbits rendered with Std Map. Take a function y = ƒ(x). An early example, which also constituted the first demonstration of the exponential divergence of chaotic trajectories, was carried out by R. H. Miller in 1964. There is a second error in the statement that r … The logistic equation is superstable at this point, which makes the Lyapunov exponent equal to negative infinity (the limit of the log function as the variable approaches zero). My feeling is that the topology will remain unchanged. For a chaotic system, the initial condition need only the orbit for a flow or from the average determinant of the chaotic map or a three dimensional chaotic flow if you know the instead of inverse iterations. millions of iterations of the differential equations to get a A parameter that discriminates among these behaviors would enable us to measure chaos. Jacobian matrix for a map) and using the fact that one exponent

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