Time series-based bifurcation diagram reconstruction

DOI10.1016/S0167-2789(99)00017-2zbMath0988.37096OpenAlexW1992147794WikidataQ126629849 ScholiaQ126629849MaRDI QIDQ1809178

Yanyan Li

Publication date: 22 July 2002

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-2789(99)00017-2

zbMATH Keywords

neural networkstime seriesprincipal component analysisbifurcation diagramsnonlinear predictors

Mathematics Subject Classification ID

Neural nets applied to problems in time-dependent statistical mechanics (82C32) Time series analysis of dynamical systems (37M10)

Related Items (1)

Modeling nonlinear dynamics and chaos: a review

Cites Work

• Unnamed Item
• Reconstructing bifurcation diagrams only from time-waveforms
• Multilayer feedforward networks are universal approximators
• Generalized one-parameter bifurcation diagram reconstruction using time series
• The Lorenz equations: bifurcations, chaos, and strange attractors
• Recognizing chaotic time-waveforms in terms of a parametrized family of nonlinear predictors
• Detection of chaotic determinism in time series from randomly forced maps
• Neural networks for nonlinear state estimation
• Deterministic Nonperiodic Flow

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