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• W4308697930 abstract "Abstract Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, the network may synchronize with the driving signal. Exploiting this synchronization, the echo-state network may be trained to autonomously reproduce the input dynamics, enabling time-series prediction. However, while synchronization is a necessary condition for prediction, it is not sufficient. Here, we study what other conditions are necessary for successful time-series prediction. We identify two key parameters for prediction performance, and conduct a parameter sweep to find regions where prediction is successful. These regions differ significantly depending on whether full or partial phase space information about the input is provided to the network during training. We explain how these regions emerge." @default.
• W4308697930 created "2022-11-14" @default.
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• W4308697930 date "2022-12-01" @default.
• W4308697930 modified "2023-10-10" @default.
• W4308697930 title "Constraints on parameter choices for successful time-series prediction with echo-state networks" @default.
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• W4308697930 doi "https://doi.org/10.1088/2632-2153/aca1f6" @default.
• W4308697930 hasPublicationYear "2022" @default.
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