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• W1487386340 endingPage "817" @default.
• W1487386340 startingPage "808" @default.
• W1487386340 abstract "An important property of Reservoir Computing, and signal processing techniques in general, is generalization and noise robustness. In trajectory generation tasks, we don’t want that a small deviation leads to an instability. For forecasting and system identification we want to avoid over-fitting. In prior work on Reservoir Computing, the addition of noise to the dynamic reservoir trajectory is generally used. In this work, we show that high-performing reservoirs can be trained using only the commonly used ridge regression. We experimentally validate these claims on two very different tasks: long-term, robust trajectory generation and system identification of a heating tank with variable dead-time." @default.
• W1487386340 created "2016-06-24" @default.
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• W1487386340 date "2008-09-05" @default.
• W1487386340 modified "2023-10-16" @default.
• W1487386340 title "Stable Output Feedback in Reservoir Computing Using Ridge Regression" @default.
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• W1487386340 doi "https://doi.org/10.1007/978-3-540-87536-9_83" @default.
• W1487386340 hasPublicationYear "2008" @default.
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