منابع مشابه
Stable Output Feedback in Reservoir Computing Using Ridge Regression
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 ...
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Output feedback is crucial for autonomous and parameterized pattern generation with reservoir networks. Read-out learning affects the output feedback loop and can lead to error amplification. Regularization is therefore important for both, generalization and reduction of error amplification. We show that regularization of the reservoir and the read-out layer reduces the risk of error amplificat...
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Reservoir computing is a recently introduced machine learning paradigm that has been shown to be well-suited for the processing of spatiotemporal data. Rather than training the network node connections and weights via backpropagation in traditional recurrent neural networks, reservoirs instead have fixed connections and weights among the ‘hidden layer’ nodes, and traditionally only the weights ...
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ژورنال
عنوان ژورنال: KI - Künstliche Intelligenz
سال: 2012
ISSN: 0933-1875,1610-1987
DOI: 10.1007/s13218-012-0187-2