Results 31 to 40 of about 233,583 (184)
Effective Behavioural Dynamic Coupling through Echo State Networks
Applied Sciences, 2019 This work presents a novel approach and paradigm for the coupling of human and robot dynamics with respect to control. We present an adaptive system based on Reservoir Computing and Recurrent Neural Networks able to couple control signals and robotic ...
Christos Melidis, Davide Marocco
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Long-Short Term Echo State Network for Time Series Prediction
IEEE Access, 2020 The Echo State Networks (ESNs) is an efficient recurrent neural network consisting of a randomly generated reservoir (a large number of neurons with sparse random recurrent connections) and a trainable linear layer.
Kaihong Zheng +5 more
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Dynamic clustering of time series with Echo State Networks [PDF]
, 2019 In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network.
A Saxena +10 more
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Dynamic Graph Echo State Networks
ESANN 2021 proceedings, 2021 Dynamic temporal graphs represent evolving relations between entities, e.g. interactions between social network users or infection spreading. We propose an extension of graph echo state networks for the efficient processing of dynamic temporal graphs, with a sufficient condition for their echo state property, and an experimental analysis of reservoir ...
Tortorella D., Micheli A.
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Training Echo State Networks with Regularization through Dimensionality Reduction [PDF]
, 2016 In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of the network on a space with lower dimensionality, before training the ...
Bianchi, Filippo Maria +2 more
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Feed-forward echo state networks [PDF]
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2006 New method for modeling nonlinear systems called the echo state networks (ESNs) has been proposed recently by H. Jaeger and H. Haas (2004). ESNs make use of the dynamics created by huge randomly created layer of recurrent units. Dynamical behavior of untrained recurrent networks was already explained in the literature and models using this behavior ...
M. Cernansky, M. Makula
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Extending stability through hierarchical clusters in Echo State Networks
Frontiers in Neuroinformatics, 2010 Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such ...
Sarah Jarvis +5 more
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Neuroevolution on the Edge of Chaos
, 2017 Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos.
Beggs John M +2 more
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On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition
Sensors, 2022 This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo ...
Gin Chong Lee, Chu Kiong Loo
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Randomness and isometries in echo state networks and compressed sensing
, 2018 Although largely different concepts, echo state networks and compressed sensing models both rely on collections of random weights; as the reservoir dynamics for echo state networks, and the sensing coefficients in compressed sensing.
Prater-Bennette, Ashley
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