Neural reconstruction of Lorenz attractors by an observable.

DOI10.1016/S0960-0779(01)00206-5zbMath1042.37025OpenAlexW1984121031MaRDI QIDQ1610113

Silvano Cincotti, Barbara Cannas

Publication date: 18 August 2002

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

Full work available at URL: https://doi.org/10.1016/s0960-0779(01)00206-5

zbMATH Keywords

observabilitynonlinear systemattractorLorenz systemlocally recurrent neural networkschaotic trajectory

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Nonlinear systems in control theory (93C10) Observability (93B07) Analytic circuit theory (94C05) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10)

Related Items (8)

Robustness of LSTM neural networks for multi-step forecasting of chaotic time series ⋮ Conjugate Lorenz-type chaotic attractors ⋮ Global attractors for semilinear damped wave equations with critical exponent on \(\mathbb{R}^n\) ⋮ The complicated trajectory behaviors in the Lorenz equation ⋮ Global attractors for semilinear hyperbolic equations ⋮ Optimizing Markovian modeling of chaotic systems with recurrent neural networks ⋮ Modeling and prediction of chaotic systems with artificial neural networks ⋮ On the homology group of chaotic manifolds and chaotic quantum field theories

Cites Work

• On global identifiability for arbitrary model parametrizations
• Performance analysis of locally recurrent neural networks
• Communication Systems via Chaotic Signals from a Reconstruction Viewpoint
• Deterministic Nonperiodic Flow
• Learning of Chua's circuit attractors by locally recurrent neural networks
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