Results 241 to 250 of about 317,020 (282)
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Pattern Recognition, 2012
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Chatzis, Sotirios P., Demiris, Yiannis
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chatzis, Sotirios P., Demiris, Yiannis
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Quaternion-Valued Echo State Networks
IEEE Transactions on Neural Networks and Learning Systems, 2015Quaternion-valued echo state networks (QESNs) are introduced to cater for 3-D and 4-D processes, such as those observed in the context of renewable energy (3-D wind modeling) and human centered computing (3-D inertial body sensors). The introduction of QESNs is made possible by the recent emergence of quaternion nonlinear activation functions with ...
Yili, Xia +2 more
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Minimum Complexity Echo State Network
IEEE Transactions on Neural Networks, 2011Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping.
Ali, Rodan, Peter, Tino
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2020
Noisy or adverse input is a threat to the safe deployment of neural networks in production. To ensure the safe operations of such networks they need to be hardened to work under such conditions. Abstract interpretation, as a tool to formally verify properties of computations, can be used for this task.
Christoph Walter Senn, Itsuo Kumazawa
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Noisy or adverse input is a threat to the safe deployment of neural networks in production. To ensure the safe operations of such networks they need to be hardened to work under such conditions. Abstract interpretation, as a tool to formally verify properties of computations, can be used for this task.
Christoph Walter Senn, Itsuo Kumazawa
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The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
In this paper we introduce the Graph Echo State Network (GraphESN) model, a generalization of the Echo State Network (ESN) approach to graph domains. GraphESNs allow for an efficient approach to Recursive Neural Networks (RecNNs) modeling extended to deal with cyclic/acyclic, directed/undirected, labeled graphs.
GALLICCHIO, CLAUDIO, MICHELI, ALESSIO
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In this paper we introduce the Graph Echo State Network (GraphESN) model, a generalization of the Echo State Network (ESN) approach to graph domains. GraphESNs allow for an efficient approach to Recursive Neural Networks (RecNNs) modeling extended to deal with cyclic/acyclic, directed/undirected, labeled graphs.
GALLICCHIO, CLAUDIO, MICHELI, ALESSIO
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Input Routed Echo State Networks
ESANN 2022 proceedings, 2022We introduce a novel Reservoir Computing (RC) approach for multi-dimensional temporal signals. Our proposal is based on routing the different dimensions of the driving input towards different dynamical sub-modules in a multi-reservoir architecture.
Argentieri, Luca +2 more
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Analysis and Design of Echo State Networks
Neural Computation, 2007The design of echo state network (ESN) parameters relies on the selection of the maximum eigenvalue of the linearized system around zero (spectral radius). However, this procedure does not quantify in a systematic manner the performance of the ESN in terms of approximation error.
Ozturk, Mustafa C. +2 more
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Echo State wireless sensor networks
2008 IEEE Workshop on Machine Learning for Signal Processing, 2008This paper addresses the question of temporal learning in spatially distributed wireless sensor networks (WSN). We propose to fuse WSNs with the echo states network learning concepts to infer the spatio-temporal dynamics of the data collaboratively measured by sensors.
Dmitriy Shutin, Gernot Kubin
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Predicting aging transition using Echo state network
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023It is generally known that in a mixture of coupled active and inactive nonlinear oscillators, the entire system may stop oscillating and become inactive if the fraction of active oscillators is reduced to a critical value. This emerging phenomenon, called the “aging transition,” can be analytically predicted from the view point of cluster ...
Biswambhar Rakshit +3 more
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Restricted Echo State Networks
2016Echo state networks are a powerful type of reservoir neural network, but the reservoir is essentially unrestricted in its original formulation. Motivated by limitations in neuromorphic hardware, we remove combinations of the four sources of memory—leaking, loops, cycles, and discrete time—to determine how these influence the suitability of the ...
Aaron Stockdill, Kourosh Neshatian
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