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Tailoring Echo State Networks for Optimal Learning [PDF]
iScience, 2020 Summary: As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing.
Pau Vilimelis Aceituno +2 more
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Echo state networks for modeling turbulent convection [PDF]
Scientific ReportsTurbulent Rayleigh-Bénard convection (RBC) is one of the very prominent examples of chaos in fluid dynamics with significant relevance in nature. Meanwhile, Echo State Networks (ESN) are among the most fundamental machine learning algorithms suited for ...
Mohammad Sharifi Ghazijahani +1 more
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Echo State Condition at the Critical Point
Entropy, 2016 Recurrent networks with transfer functions that fulfil the Lipschitz continuity with K = 1 may be echo state networks if certain limitations on the recurrent connectivity are applied.
Norbert Michael Mayer
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Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks [PDF]
Sensors, 2020 The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through ...
Tommaso Pecorella +2 more
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Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks [PDF]
Frontiers in Computational Neuroscience, 2021 Recurrent cortical networks provide reservoirs of states that are thought to play a crucial role for sequential information processing in the brain.
Fabian Schubert, Claudius Gros
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Fuzzy-Weighted Echo State Networks
Frontiers in Energy Research, 2022 A novel echo state network (ESN), referred to as a fuzzy-weighted echo state network (FWESN), is proposed by using the structural information of data sets to improve the performance of the classical ESN. The information is incorporated into the classical
Zhao Yao, Zhao Yao, Yingshun Li
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Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere. [PDF]
Sci Rep, 2019 Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure.
Verzelli P, Alippi C, Livi L.
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Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks [PDF]
Sensors, 2017 A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve.
Luciano Sánchez +3 more
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Transferring learning from external to internal weights in echo-state networks with sparse connectivity. [PDF]
PLoS ONE, 2012 Modifying weights within a recurrent network to improve performance on a task has proven to be difficult. Echo-state networks in which modification is restricted to the weights of connections onto network outputs provide an easier alternative, but at the
David Sussillo, L F Abbott
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Deep echo state networks in data marketplaces
Machine Learning with Applications, 2023 Data Marketplaces are the digital platform for data buyers and data sellers to trade information as valuable products or items. The expectation taken for granted from the users of a data marketplace is the truth of the exchanged information. However, the
Will Serrano
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