The architecture of dynamic reservoir in the echo state network

DOI10.1063/1.4746765zbMath1319.92006OpenAlexW2124349412WikidataQ50935904 ScholiaQ50935904MaRDI QIDQ2944591

Lixiang Li, Xiang Liu, Hongyan Cui

Publication date: 2 September 2015

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

Full work available at URL: https://doi.org/10.1063/1.4746765

zbMATH Keywords

echo state networkdynamic reservoirsmall-world topologyscale-free topology

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (3)

Global optimization of hyper-parameters in reservoir computing ⋮ Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network ⋮ Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing

Cites Work

• Reservoir computing approaches to recurrent neural network training
• Structural and functional clusters of complex brain networks
• Renormalization group analysis of the small-world network model
• NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches
• Optimization and applications of echo state networks with leaky- integrator neurons
• Super-spreaders and the rate of transmission of the SARS virus
• Emergence of Scaling in Random Networks
• Critical behavior of blind spots in sensor networks
• Synchronization and desynchronization of complex dynamical networks: an engineering viewpoint
• Collective dynamics of ‘small-world’ networks

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