2002
DOI: 10.1016/s0167-2789(02)00622-x
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Parry measure and the topological entropy of chaotic repellers embedded within chaotic attractors

Abstract: We study the topological entropy of chaotic repellers formed by those points in a given chaotic attractor that never visit some small forbidden hole-region in the phase space. The hole is a set of points in the phase space that have a sequence α = (α 0 α 1 . . . α l−1 ) as the first l letters in their itineraries. We point out that the difference between the topological entropies of the attractor and the embedded repeller is for most choices of α approximately equal to the Parry measure corresponding to α, µ P… Show more

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Cited by 4 publications
(2 citation statements)
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“…This approach is based on the technique developed in [2] and is, to the best of our knowledge, the first general approach to the studies of open systems based on topological dynamics, rather than a standard approach which is concerned with metric properties and deals with the construction of conditionally invariant measures, escape rates, etc (see, e.g., [3]). Some (essentially inconclusive) numerical studies of the relations between the topological entropies of chaotic attractors and repellers embedded into them were made in [9] 3 .…”
Section: Introductionmentioning
confidence: 99%
“…This approach is based on the technique developed in [2] and is, to the best of our knowledge, the first general approach to the studies of open systems based on topological dynamics, rather than a standard approach which is concerned with metric properties and deals with the construction of conditionally invariant measures, escape rates, etc (see, e.g., [3]). Some (essentially inconclusive) numerical studies of the relations between the topological entropies of chaotic attractors and repellers embedded into them were made in [9] 3 .…”
Section: Introductionmentioning
confidence: 99%
“…This rests on some theorems of Parry (1964Parry ( , 1966 showing that, if a series behaves locally like a Markov process, then, from the perspective of information theory, it is Markov. This approach has been extended by Buljan & Paar (2002).…”
Section: Relaxation Of Metric Assumptionsmentioning
confidence: 99%