Modeling and prediction of chaotic systems with artificial neural networks

DOI10.1002/FLD.2117zbMath1193.65213OpenAlexW2129435924MaRDI QIDQ3578176

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Publication date: 13 July 2010

Published in: International Journal for Numerical Methods in Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/fld.2117

zbMATH Keywords

predictionalgorithmnumerical examplestime seriesartificial neural networksbackpropagationLyapunov exponentchaotic systemLorenz's equationsmulti-layered neural networkNARMAX method

Mathematics Subject Classification ID

Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10) Numerical chaos (65P20) Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25)

Related Items (1)

Robustness of LSTM neural networks for multi-step forecasting of chaotic time series

Cites Work

• Learning chaotic dynamics by neural networks
• Modelling and prediction of machining errors using ARMAX and NARMAX structures
• Neural reconstruction of Lorenz attractors by an observable.
• Representations of non-linear systems: the NARMAX model
• Generalized Predictive Control of Discrete-Time Chaotic Systems
• A logical calculus of the ideas immanent in nervous activity

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