Download Analysis of Observed Chaotic Data by Henry Abarbanel PDF

By Henry Abarbanel

When I encountered the belief of chaotic habit in deterministic dynami­ cal platforms, it gave me either nice pause and nice reduction. The beginning of the nice aid was once paintings I had performed prior on renormalization crew homes of homogeneous, isotropic fluid turbulence. on the time I labored on that, it was once favourite to ascribe the it appears stochastic nature of turbulent flows to a few type of stochastic using of the fluid at huge scales. It was once easily no longer imagined that with only deterministic riding the fluid should be turbulent from its personal chaotic movement. I keep in mind a colleague remarking that there has been whatever essentially unsettling approximately requiring a fluid to be pushed stochastically to have even the illusion of complicated movement within the pace and strain fields. I definitely agreed with him, yet neither people have been capable of supply the other average advice for the saw, it appears stochastic motions of the turbulent fluid. So it used to be with reduction that chaos in nonlinear structures, particularly, advanced evolution, indistinguish­ capable from stochastic motions utilizing regular instruments reminiscent of Fourier research, seemed in my bag of physics notions. It enabled me to have a physi­ cally average conceptual framework during which to anticipate deterministic, but stochastic having a look, motions. the good pause got here from no longer understanding what to make of chaos in non­ linear systems.

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If we were to choose T = Ts = 1 ns, we will not have seen any of the dynamics unfold in that time, and we should expect from a physical point of view that s(n) and s(n + T) are for all practical purposes the same measurement. III Finally, since chaotic systems are intrinsically unstable, if T is too large, any connection between the measurements s (n) and s (n + T) is numerically tantamount to being random with respect to each other. Even very accurate determinations of the value of s(n) cannot prevent the exponential growth of small errors characteristic of chaos from decorrelating it from the measurement T steps later, when T becomes large.

7) and an additional component s(n + Td) ? 4 A Few Remarks About I( T) 37 choosing the set of measurements A to be the collection of d-dimensional vectors y(n) and the measurements B the measurements s(n + dT). This would gives us the answer to the question, how much is learned about the measurement s(n+dT) from the measurements ofy(n) on the average over the attractor? If this number were small, relative to the value at T = 0 for example, then we would conclude the d-dimensional vector y(n) was capturing the independent coordinates of the source using the time lag T.

The use of mutual information produced a clear signal of propagating information in a lattice of coupled nonlinear oscillators while polynomial correlation functions of orders up to quartic showed essentially no signal. While we do not pursue this facet of mutual information, it is one more indication that information is a key property of nonlinear systems which we should exploit to learn about their inner workings. 1 Global Embedding Dimension dE We wish to determine the integer global dimension where we have the necessary number of coordinates to unfold observed orbits from self overlaps arising from projection of the attractor to a lower dimensional space.

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