Modeling chaotic systems: dynamical equations vs machine learning approach

DOI10.1016/J.CNSNS.2022.106452zbMath1502.37086OpenAlexW4283161378WikidataQ114196444 ScholiaQ114196444MaRDI QIDQ2160910

Jie Zhang, Michael Small, Tongfeng Weng, Hui-Jie Yang

Publication date: 3 August 2022

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cnsns.2022.106452

zbMATH Keywords

recurrencesynchronizationchaotic systemsreservoir computing approach

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Simulation of dynamical systems (37M05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Dynamical systems in numerical analysis (37N30) Computational aspects of data analysis and big data (68T09)

Related Items (1)

Synchronization of reservoir computing models via a nonlinear controller

Uses Software

• K2

Cites Work

• Reservoir computing approaches to recurrent neural network training
• Laplacian energy of a graph
• The synchronization of chaotic systems
• Echo state networks are universal
• RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS
• Recurrence networks—a novel paradigm for nonlinear time series analysis
• Superfamily phenomena and motifs of networks induced from time series
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
• Defining chaos
• Deterministic Nonperiodic Flow
• Nonlinear Time Series Analysis
• Synchronization in chaotic systems
• Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing
• Echo State Property Linked to an Input: Exploring a Fundamental Characteristic of Recurrent Neural Networks

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