Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

DOI10.1063/1.4905458zbMath1345.37089OpenAlexW1990241300WikidataQ39044930 ScholiaQ39044930MaRDI QIDQ2821586

Hiroshi Gotoda, Irving R. Epstein, Pier Luigi Gentili, Milos Dolnik

Publication date: 21 September 2016

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

Full work available at URL: https://semanticscholar.org/paper/20c08165023346f3429060b047e43e3ce18d7da7

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Time series analysis of dynamical systems (37M10)

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Cites Work

• Practical method for determining the minimum embedding dimension of a scalar time series
• A practical method for calculating largest Lyapunov exponents from small data sets
• Practical implementation of nonlinear time series methods: The <scp>TISEAN</scp> package
• Independent coordinates for strange attractors from mutual information
• Recent Developments in Chaotic Time Series Analysis
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