Machine Learning With Echo State Networks
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- Machine Learning With Echo State Networks Y. Xu 2020 🛫 2023-06-20 !!2 citation reading link todoist Print:: ❌ Zotero Link:: ResearchGate Link; Xu_2020_Machine Learning With Echo State Networks.pdf PDF:: NA Files:: ResearchGate Link; Xu_2020_Machine Learning With Echo State Networks.pdf Reading Note:: Web Rip::
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This report aims at presenting a detailed technical review of Recurrent Neural Networks (RNNs) and in particular Echo State Networks (ESNs), which are networks belonging to the general class of Reservoir Computing. A general overview of RNNs is presented, followed by a detailed discussion of Reservoir Computing and Echo State Networks. We discuss the working principle, construction details, applications and limitations of ESNs. Current research work in ESNs directed towards overcoming some of the limitations faced by researchers using ESNs, is reviewed. One major limitation of ESNs is their dependence on the hyper-parameters. The stable operating region of ESNs in the hyper-parameter space is quite narrow, which makes training these networks a challenging task. The paper reviewed proposes a novel model of ESNs, which prevents the network from entering chaotic regime. The paper also demonstrates the improved performance in the experiments performed over standard benchmarking datasets for nonlinear systems. Finally, we conclude with a discussion of renewed interest in ESNs, in particular, as a computational principle.
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