Results 31 to 40 of about 317,020 (282)
Network Traffic Prediction Method Based on Improved Echo State Network
IEEE Access, 2018 The network traffic sequence has the complex characters, such as mutability, chaos, timeliness, and nonlinearity, which bring many difficulties to network traffic prediction.
Jian Zhou +4 more
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Complex & Intelligent Systems, 2023 Reliable mechanical fault diagnosis of high-voltage circuit breakers is important to ensure the safety of electric power systems. Recent fault diagnosis approaches are mostly based on a single classifier whose performance relies heavily on expert prior ...
Xiaofeng Li +4 more
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Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting
Mathematics, 2022 Time series forecasting provides a vital basis for the control and management of various systems. The time series data in the real world are usually strongly nonstationary and nonlinear, which increases the difficulty of reliable forecasting.
Wen-Jie Liu +4 more
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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 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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Detection of Coronavirus Phishing Emails using Echo State Neural Network
Zanco Journal of Pure and Applied Sciences, 2020 E-mail is an important and fast mean of conveying information among people, banks, companies and organizations, that information is often important, sensitive and secret, this make it worthy to attackers who can use it for harmful purposes.
omar younis
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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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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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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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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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