Path length statistics in reservoir computers

DOI10.1063/5.0014643zbMath1490.68105OpenAlexW3049522084WikidataQ98886557 ScholiaQ98886557MaRDI QIDQ5129880

Thomas L. Carroll

Publication date: 2 November 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/5.0014643

Mathematics Subject Classification ID

Time series analysis of dynamical systems (37M10) Other nonclassical models of computation (68Q09) Networks and circuits as models of computation; circuit complexity (68Q06)

Related Items (6)

Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing ⋮ Breaking symmetries of the reservoir equations in echo state networks ⋮ Reservoir computing with swarms ⋮ Transfer learning of chaotic systems ⋮ On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD ⋮ Low dimensional manifolds in reservoir computers

Uses Software

• Stony Brook

Cites Work

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• Principal component analysis.
• Statistical mechanics of complex networks
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Deterministic Nonperiodic Flow
• Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing
• Dimension of reservoir computers
• Collective dynamics of ‘small-world’ networks

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