Search results for "Chaotic dynamics"

showing 10 items of 197 documents

Looking More Closely at the Rabinovich-Fabrikant System

2016

Recently, we looked more closely into the Rabinovich–Fabrikant system, after a decade of study [Danca & Chen, 2004], discovering some new characteristics such as cycling chaos, transient chaos, chaotic hidden attractors and a new kind of saddle-like attractor. In addition to extensive and accurate numerical analysis, on the assumptive existence of heteroclinic orbits, we provide a few of their approximations.

Control of chaosheteroclinic orbitLIL numerical methodApplied Mathematicsta111Chaotictransient chaos01 natural sciencesRabinovich-Fabrikant system010305 fluids & plasmasNonlinear Sciences::Chaotic DynamicsClassical mechanicsModeling and Simulation0103 physical sciencesAttractorHeteroclinic orbitStatistical physicscycling chaos010301 acousticsEngineering (miscellaneous)MathematicsInternational Journal of Bifurcation and Chaos
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Scaling behaviour of non-hyperbolic coupled map lattices

2006

Coupled map lattices of non-hyperbolic local maps arise naturally in many physical situations described by discretised reaction diffusion equations or discretised scalar field theories. As a prototype for these types of lattice dynamical systems we study diffusively coupled Tchebyscheff maps of N-th order which exhibit strongest possible chaotic behaviour for small coupling constants a. We prove that the expectations of arbitrary observables scale with \sqrt{a} in the low-coupling limit, contrasting the hyperbolic case which is known to scale with a. Moreover we prove that there are log-periodic oscillations of period \log N^2 modulating the \sqrt{a}-dependence of a given expectation value.…

Coupling constantDynamical systems theoryPhase spaceMathematical analysisReaction–diffusion systemFOS: Physical sciencesObservableExpectation valueChaotic Dynamics (nlin.CD)Nonlinear Sciences - Chaotic DynamicsScalar fieldScalingMathematics
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The pianigiani-yorke measure for topological markov chains

1997

We prove the existence of a Pianigiani-Yorke measure for a Markovian factor of a topological Markov chain. This measure induces a Gibbs measure in the limit set. The proof uses the contraction properties of the Ruelle-Perron-Frobenius operator.

Discrete mathematicsMathematics::Dynamical SystemsMarkov chain mixing timeMarkov chainGeneral MathematicsMarkov processPartition function (mathematics)TopologyHarris chainNonlinear Sciences::Chaotic Dynamicssymbols.namesakeBalance equationsymbolsExamples of Markov chainsGibbs measureMathematicsIsrael Journal of Mathematics
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A fractal set from the binary reflected Gray code

2005

The permutation associated with the decimal expression of the binary reflected Gray code with $N$ bits is considered. Its cycle structure is studied. Considered as a set of points, its self-similarity is pointed out. As a fractal, it is shown to be the attractor of a IFS. For large values of $N$ the set is examined from the point of view of time series analysis

Discrete mathematicsPermutation (music)FísicaGeneral Physics and AstronomyBinary numberFOS: Physical sciencesStatistical and Nonlinear PhysicsNonlinear Sciences - Chaotic DynamicsDecimalGray codeSet (abstract data type)FractalAttractorPoint (geometry)Chaotic Dynamics (nlin.CD)Mathematical PhysicsMathematics
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Construction of chaotic dynamical system

2010

The first‐order difference equation xn+ 1 = f(xn ), n = 0,1,…, where f: R → R, is referred as an one‐dimensional discrete dynamical system. If function f is a chaotic mapping, then we talk about chaotic dynamical system. Models with chaotic mappings are not predictable in long‐term. In this paper we consider family of chaotic mappings in symbol space S 2. We use the idea of topological semi‐conjugacy and so we can construct a family of mappings in the unit segment such that it is chaotic. First published online: 09 Jun 2011

Discrete mathematicsPure mathematicsincreasing mappingDifferential equationChaoticinfinite symbol spaceBinary numberFunction (mathematics)Space (mathematics)Nonlinear Sciences::Chaotic Dynamicstopological semi‐conjugacyModeling and SimulationQA1-939Orbit (dynamics)chaotic mappingbinary expansionUnit (ring theory)MathematicsAnalysisMathematicsCoupled map latticeMathematical Modelling and Analysis
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Massive evaluation and analysis of Poincar�� recurrences on grids of initial data: a tool to map chaotic diffusion

2020

We present a novel numerical method aimed to characterize global behaviour, in particular chaotic diffusion, in dynamical systems. It is based on an analysis of the Poincar\'e recurrence statistics on massive grids of initial data or values of parameters. We concentrate on Hamiltonian systems, featuring the method separately for the cases of bounded and non-bounded phase spaces. The embodiments of the method in each of the cases are specific. We compare the performances of the proposed Poincar\'e recurrence method (PRM) and the custom Lyapunov exponent (LE) methods and show that they expose the global dynamics almost identically. However, a major advantage of the new method over the known g…

Dynamical systems theoryComputer scienceChaoticGeneral Physics and AstronomyFOS: Physical sciencesLyapunov exponent01 natural sciences010305 fluids & plasmasHamiltonian systemsymbols.namesakeSimple (abstract algebra)0103 physical sciencesApplied mathematicsDiffusion (business)010306 general physicsInstrumentation and Methods for Astrophysics (astro-ph.IM)ComputingMilieux_MISCELLANEOUSEarth and Planetary Astrophysics (astro-ph.EP)Numerical analysisNonlinear Sciences - Chaotic DynamicsHardware and ArchitectureBounded functionsymbolsChaotic Dynamics (nlin.CD)Astrophysics - Instrumentation and Methods for Astrophysics[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]Astrophysics - Earth and Planetary Astrophysics
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A wavelet-based tool for studying non-periodicity

2010

This paper presents a new numerical approach to the study of non-periodicity in signals, which can complement the maximal Lyapunov exponent method for determining chaos transitions of a given dynamical system. The proposed technique is based on the continuous wavelet transform and the wavelet multiresolution analysis. A new parameter, the \textit{scale index}, is introduced and interpreted as a measure of the degree of the signal's non-periodicity. This methodology is successfully applied to three classical dynamical systems: the Bonhoeffer-van der Pol oscillator, the logistic map, and the Henon map.

Dynamical systems theoryFOS: Physical sciencesLyapunov exponentDynamical Systems (math.DS)37D99 42C40WaveletsDynamical systemMeasure (mathematics)symbols.namesakeWaveletModelling and SimulationFOS: MathematicsApplied mathematicsMathematics - Dynamical SystemsContinuous wavelet transformMathematicsMathematical analysisNonlinear Sciences - Chaotic DynamicsNon-periodicityHénon mapNonlinear Sciences::Chaotic DynamicsComputational MathematicsComputational Theory and MathematicsModeling and SimulationsymbolsLogistic mapChaotic Dynamics (nlin.CD)Chaotic dynamical systems
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Short chaotic strings and their behaviour in the scaling region

2008

Coupled map lattices are a paradigm of higher-dimensional dynamical systems exhibiting spatio-temporal chaos. A special case of non-hyperbolic maps are one-dimensional map lattices of coupled Chebyshev maps with periodic boundary conditions, called chaotic strings. In this short note we show that the fine structure of the self energy of this chaotic string in the scaling region (i.e. for very small coupling) is retained if we reduce the length of the string to three lattice points.

Dynamical systems theoryGeneral MathematicsApplied MathematicsChaoticFOS: Physical sciencesGeneral Physics and AstronomyStatistical and Nonlinear PhysicsTopologyNonlinear Sciences - Chaotic DynamicsChebyshev filterString (physics)Coupling (physics)Periodic boundary conditionsStatistical physicsChaotic Dynamics (nlin.CD)ScalingMathematicsCoupled map lattice
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Analytical properties of horizontal visibility graphs in the Feigenbaum scenario

2012

Time series are proficiently converted into graphs via the horizontal visibility (HV) algorithm, which prompts interest in its capability for capturing the nature of different classes of series in a network context. We have recently shown [1] that dynamical systems can be studied from a novel perspective via the use of this method. Specifically, the period-doubling and band-splitting attractor cascades that characterize unimodal maps transform into families of graphs that turn out to be independent of map nonlinearity or other particulars. Here we provide an in depth description of the HV treatment of the Feigenbaum scenario, together with analytical derivations that relate to the degree di…

Dynamical systems theoryMatemáticasGeneral Physics and AstronomyFOS: Physical sciencesLyapunov exponentDynamical Systems (math.DS)Fixed point01 natural sciencesAeronáutica010305 fluids & plasmassymbols.namesakeBifurcation theoryOscillometry0103 physical sciencesAttractorFOS: MathematicsEntropy (information theory)Computer SimulationStatistical physicsMathematics - Dynamical Systems010306 general physicsMathematical PhysicsMathematicsSeries (mathematics)Degree (graph theory)Applied MathematicsStatistical and Nonlinear Physics16. Peace & justiceNonlinear Sciences - Chaotic DynamicsNonlinear DynamicsPhysics - Data Analysis Statistics and ProbabilitysymbolsChaotic Dynamics (nlin.CD)AlgorithmsData Analysis Statistics and Probability (physics.data-an)
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Feigenbaum graphs: a complex network perspective of chaos

2011

The recently formulated theory of horizontal visibility graphs transforms time series into graphs and allows the possibility of studying dynamical systems through the characterization of their associated networks. This method leads to a natural graph-theoretical description of nonlinear systems with qualities in the spirit of symbolic dynamics. We support our claim via the case study of the period-doubling and band-splitting attractor cascades that characterize unimodal maps. We provide a universal analytical description of this classic scenario in terms of the horizontal visibility graphs associated with the dynamics within the attractors, that we call Feigenbaum graphs, independent of map…

Dynamical systems theoryScienceSymbolic dynamicsFOS: Physical sciencesLyapunov exponentFixed pointBioinformatics01 natural sciences010305 fluids & plasmasStatistical Mechanicssymbols.namesake0103 physical sciencesAttractorEntropy (information theory)Statistical physics010306 general physicsChaotic SystemsCondensed-Matter PhysicsCondensed Matter - Statistical MechanicsPhysicsMultidisciplinaryStatistical Mechanics (cond-mat.stat-mech)Applied MathematicsPhysicsQRComplex SystemsComplex networkNonlinear Sciences - Chaotic DynamicsDegree distributionNonlinear DynamicssymbolsMedicineChaotic Dynamics (nlin.CD)MathematicsAlgorithmsResearch Article
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