Optimizing Markovian modeling of chaotic systems with recurrent neural networks

DOI10.1016/J.CHAOS.2006.10.018zbMath1142.90458OpenAlexW2005607935MaRDI QIDQ944855

Denise R. Pechmann, Adelmo L. Cechin, Luiz P. L. de Oliveira

Publication date: 10 September 2008

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2006.10.018

Mathematics Subject Classification ID

Stochastic programming (90C15) Dynamical systems in biology (37N25) Applications of branching processes (60J85)

Related Items (3)

Robustness of LSTM neural networks for multi-step forecasting of chaotic time series ⋮ High-efficiency chaotic time series prediction based on time convolution neural network ⋮ A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations

Cites Work

• Neural reconstruction of Lorenz attractors by an observable.
• Multilayer feedforward networks are universal approximators
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