Search results for "Chaotic dynamics"

showing 10 items of 197 documents

Subharmonic and homoclinic bifurcations in the driven and damped sine-Gordon system

1999

Abstract Chaotic responses induced by an applied biharmonic driven signal on the sine-Gordon (sG) system influenced by a constant dc-driven and the damping fields are investigated using a collective coordinate approach for the motion of the breather in the system. For this biharmonic signal, one term has a large amplitude at low frequency. Thus, the classical Melnikov method does not apply to such a system; however, we use the modified version of the Melnikov method to homoclinic bifurcations of the perturbed sG system. Additionally resonant breathers are studied using the modified subharmonic Melnikov theory. This dynamic behavior is illustrated by some numerical computations.

BreatherMathematical analysisChaoticStatistical and Nonlinear PhysicsCondensed Matter PhysicsSignalNonlinear Sciences::Chaotic DynamicsAmplitudeClassical mechanicsBiharmonic equationHomoclinic orbitSineConstant (mathematics)Nonlinear Sciences::Pattern Formation and SolitonsMathematicsPhysica D: Nonlinear Phenomena
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A Review of Mathematical and Computational Methods in Cancer Dynamics.

2022

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the si…

Cancer Researchinverse problemssystems oncologyFOS: Physical sciencescomplex networksdynamical systemsOther Quantitative Biology (q-bio.OT)Nonlinear Sciences - Chaotic DynamicsalgorithmsQuantitative Biology - Other Quantitative BiologyOncologyFOS: Biological sciencescancerChaotic Dynamics (nlin.CD)complexity scienceinformation theory
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Exchange rates expectations and chaotic dynamics: a replication study

2018

Abstract In this paper the author analyzes the behavior of exchange rates expectations for four currencies, by considering a re-calculation and an extension of Resende and Zeidan (Expectations and chaotic dynamics: empirical evidence on exchange rates, Economics Letters, 2008). Considering Lyapunov exponent-based tests results, they are not supportive of chaos in exchange rates expectations, although the so-called 0–1 test strongly supports the chaos hypothesis.

ChaoticSocial SciencesLyapunov exponent01 natural sciencesexchange rates010305 fluids & plasmassymbols.namesakeH0502 economics and business0103 physical sciencesReplication (statistics)ddc:330Statistical physicsC15050207 economicsEmpirical evidenceHB71-74MathematicsC120-1 testdeterministic chaos05 social sciencesDynamics (mechanics)Lyapunov exponentsNonlinear Sciences::Chaotic DynamicsEconomics as a sciencesymbolsGeneral Economics Econometrics and Financeexpectations
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Hidden and self-excited attractors in radiophysical and biophysical models

2017

One of the central tasks of investigation of dynamical systems is the problem of analysis of the steady (limiting) behavior of the system after the completion of transient processes, i.e., the problem of localization and analysis of attractors (bounded sets of states of the system to which the system tends after transient processes from close initial states). Transition of the system with initial conditions from the vicinity of stationary state to an attractor corresponds to the case of a self-excited attractor. However, there exist attractors of another type: hidden attractors are attractors with the basin of attraction which does not have intersection with a small neighborhoods of any equ…

Chua circuitskaaosteoriapancreatic beta-cellvirtapiiritattraktoritradiophysical generatoroskillaattoritbiofysiikkaNonlinear Sciences::Chaotic Dynamicshidden attractorsbifurkaatiosäteilyfysiikkamultistabilityself-excited attractorskatastrofiteoriamatemaattiset mallitdifferentiaaliyhtälöt
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Coherence resonance in Bonhoeffer-Van der Pol circuit

2009

International audience; A nonlinear electronic circuit simulating the neuronal activity in a noisy environment is proposed. This electronic circuit is exactly ruled by the set of Bonhoeffer-Van Der Pol equations and is excited with a Gaussian noise. Without external deterministic stimuli, it is shown that the circuit exhibits the so-called 'coherence resonance' phenomenon.

Circuit design[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]02 engineering and technology01 natural sciencesResonance (particle physics)symbols.namesakeComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologiesControl theoryQuantum mechanics0103 physical sciences0202 electrical engineering electronic engineering information engineeringElectrical and Electronic Engineering010306 general physicsMathematicsElectronic circuitVan der Pol oscillatorAmplifier020208 electrical & electronic engineering[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsNonlinear systemGaussian noise[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]symbolsRLC circuit
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Approximating hidden chaotic attractors via parameter switching.

2018

In this paper, the problem of approximating hidden chaotic attractors of a general class of nonlinear systems is investigated. The parameter switching (PS) algorithm is utilized, which switches the control parameter within a given set of values with the initial value problem numerically solved. The PS-generated attractor approximates the attractor obtained by averaging the control parameter with the switched values, which represents the hidden chaotic attractor. The hidden chaotic attractors of a generalized Lorenz system and the Rabinovich-Fabrikant system are simulated for illustration. In Refs. 1–3, it is proved that the attractors of a chaotic system, considered as the unique numerical …

Class (set theory)Mathematics::Dynamical SystemsChaoticGeneral Physics and AstronomyFOS: Physical sciences01 natural sciences010305 fluids & plasmasSet (abstract data type)phase space methods0103 physical sciencesAttractorApplied mathematicsInitial value problemdifferentiaalilaskenta010301 acousticsMathematical PhysicsMathematicsApplied Mathematicsta111numerical approximationsStatistical and Nonlinear Physicschaotic systemsLorenz systemchaoticNonlinear Sciences - Chaotic DynamicsNonlinear Sciences::Chaotic DynamicsNonlinear systemkaaosnumeerinen analyysinonlinear systemsChaotic Dynamics (nlin.CD)Chaos (Woodbury, N.Y.)
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Active spike transmission in the neuron model with a winding threshold manifold

2012

International audience; We analyze spiking responses of excitable neuron model with a winding threshold manifold on a pulse stimulation. The model is stimulated with external pulse stimuli and can generate nonlinear integrate-and-fire and resonant responses typical for excitable neuronal cells (all-or-none). In addition we show that for certain parameter range there is a possibility to trigger a spiking sequence with a finite number of spikes (a spiking message) in the response on a short stimulus pulse. So active transformation of N incoming pulses to M (with M>N) outgoing spikes is possible. At the level of single neuron computations such property can provide an active "spike source" comp…

Cognitive Neuroscience[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS][ MATH.MATH-DS ] Mathematics [math]/Dynamical Systems [math.DS][ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]Threshold manifoldBiological neuron modelMachine learningcomputer.software_genreTopology01 natural sciences010305 fluids & plasmaslaw.inventionSpike encodingArtificial Intelligencelaw0103 physical sciences010306 general physicsSpike transmissionActive responseBifurcationMathematicsExcitabilityQuantitative Biology::Neurons and Cognitionbusiness.industry[SCCO.NEUR]Cognitive science/NeuroscienceDissipationComputer Science ApplicationsPulse (physics)[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsNonlinear systemTransmission (telecommunications)Nonlinear dynamics[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SCCO.NEUR ] Cognitive science/NeuroscienceSpike (software development)Artificial intelligencebusinessManifold (fluid mechanics)computer
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Prediction of Hidden Oscillations Existence in Nonlinear Dynamical Systems: Analytics and Simulation

2013

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure, for localization of hidden attracto…

Computer scienceOscillationbusiness.industryProcess (computing)State (functional analysis)Machine learningcomputer.software_genreManifoldNonlinear Sciences::Chaotic DynamicsAttractorTrajectoryPoint (geometry)Transient (oscillation)Artificial intelligenceStatistical physicsbusinesscomputer
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Hidden attractors on one path : Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems

2017

In this report, by the numerical continuation method we visualize and connect hidden chaotic sets in the Glukhovsky-Dolzhansky, Lorenz and Rabinovich systems using a certain path in the parameter space of a Lorenz-like system.

Computer sciencechaosChaoticFOS: Physical sciencesPhysics::Data Analysis; Statistics and ProbabilityParameter space01 natural sciences010305 fluids & plasmasRabinovich systemLorenz system0103 physical sciencesAttractorGlukhovsky–Dolzhansky systemApplied mathematics010301 acousticsEngineering (miscellaneous)kaaosteoriaApplied Mathematicsta111Lorenz-like systemNonlinear Sciences - Chaotic DynamicsNonlinear Sciences::Chaotic DynamicsNumerical continuationModeling and SimulationPath (graph theory)numeerinen analyysiChaotic Dynamics (nlin.CD)hidden attractorInternational Journal of Bifurcation and Chaos
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Chaos in two-dimensional Kepler problem with spin-orbit coupling

2017

We consider classical two-dimensional Kepler system with spin-orbit coupling and show that at a sufficiently strong coupling it demonstrates a chaotic behavior. The chaos emerges since the spin-orbit coupling reduces the number of the integrals of motion as compared to the number of the degrees of freedom. This reduction is manifested in the equations of motion as the emergence of the anomalous velocity determined by the spin orientation. By using analytical and numerical arguments, we demonstrate that the chaotic behavior, being driven by this anomalous term, is related to the system energy dependence on the initial spin orientation. We observe the critical dependence of the dynamics on th…

Condensed Matter - Mesoscale and Nanoscale PhysicsMesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciencesChaotic Dynamics (nlin.CD)Nonlinear Sciences - Chaotic Dynamics
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