Identifying the linear region based on machine learning to calculate the largest Lyapunov exponent from chaotic time series

DOI10.1063/1.5065373zbMath1404.37098OpenAlexW2903903806WikidataQ90838361 ScholiaQ90838361MaRDI QIDQ4644743

Shuang Zhou, Xing-Yuan Wang

Publication date: 8 January 2019

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/1.5065373

Mathematics Subject Classification ID

Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25)

Related Items (4)

Dynamics and complexity analysis of fractional-order chaotic systems with line equilibrium based on Adomian decomposition ⋮ A novel method based on the pseudo-orbits to calculate the largest Lyapunov exponent from chaotic equations ⋮ Control and anti-control of chaos based on the moving largest Lyapunov exponent using reinforcement learning ⋮ A New Image Encryption Algorithm with Cantor Diagonal Scrambling Based on the PUMCML System

Cites Work

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• A two-dimensional mapping with a strange attractor
• Determining Lyapunov exponents from a time series
• Practical method for determining the minimum embedding dimension of a scalar time series
• Computation of the largest positive Lyapunov exponent using rounding mode and recursive least square algorithm
• On the analysis of pseudo-orbits of continuous chaotic nonlinear systems simulated using discretization schemes in a digital computer
• On the use of interval extensions to estimate the largest Lyapunov exponent from chaotic data
• Output formation-containment of interacted heterogeneous linear systems by distributed hybrid active control
• A practical method for calculating largest Lyapunov exponents from small data sets
• An equation for continuous chaos
• A Very Simple Method to Calculate the (Positive) Largest Lyapunov Exponent Using Interval Extensions
• A new test for chaos in deterministic systems
• Independent coordinates for strange attractors from mutual information
• Ergodic theory of chaos and strange attractors
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
• Deterministic Nonperiodic Flow
• Oscillation and Chaos in Physiological Control Systems
• Simple mathematical models with very complicated dynamics

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