Evaluating Lyapunov exponent spectra with neural networks
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Publication:400825
DOI10.1016/j.chaos.2013.03.001zbMath1294.37031OpenAlexW2122474557MaRDI QIDQ400825
Julien Clinton Sprott, Arne Maus
Publication date: 26 August 2014
Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.chaos.2013.03.001
Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25) Dimension theory of smooth dynamical systems (37C45)
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Uses Software
Cites Work
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- Neural network method for determining embedding dimension of a time series
- A two-dimensional mapping with a strange attractor
- Determining Lyapunov exponents from a time series
- An algorithm for the \(n\) Lyapunov exponents of an \(n\)-dimensional unknown dynamical system
- Multilayer feedforward networks are universal approximators
- The topological invariance of Lyapunov exponents in embedded dynamics
- Practical implementation of nonlinear time series methods: The <scp>TISEAN</scp> package
- The Identification of Spurious Lyapunov Exponents in Jacobian Algorithms
- CHARACTERISTIC LYAPUNOV EXPONENTS AND SMOOTH ERGODIC THEORY
- IDENTIFICATION OF TRUE AND SPURIOUS LYAPUNOV EXPONENTS FROM TIME SERIES
- Lyapunov exponents from observed time series
- Ergodic theory of chaos and strange attractors
- Spurious Lyapunov Exponents Computed from Data
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