A review of designs and applications of echo state networks
Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …
A systematic review of echo state networks from design to application
A recurrent neural network (RNN) has demonstrated its outstanding ability in sequence
tasks and has achieved state of the art in many applications, such as industrial and medical …
tasks and has achieved state of the art in many applications, such as industrial and medical …
Growing echo-state network with multiple subreservoirs
J Qiao, F Li, H Han, W Li - IEEE transactions on neural …, 2016 - ieeexplore.ieee.org
An echo-state network (ESN) is an effective alternative to gradient methods for training
recurrent neural network. However, it is difficult to determine the structure (mainly the …
recurrent neural network. However, it is difficult to determine the structure (mainly the …
Adaptive elastic echo state network for multivariate time series prediction
M Xu, M Han - IEEE transactions on cybernetics, 2016 - ieeexplore.ieee.org
Echo state network (ESN) is a new kind of recurrent neural network with a randomly
generated reservoir structure and an adaptable linear readout layer. It has been widely …
generated reservoir structure and an adaptable linear readout layer. It has been widely …
Bayesian optimized echo state network applied to short-term load forecasting
G Trierweiler Ribeiro, J Guilherme Sauer… - Energies, 2020 - mdpi.com
Load forecasting impacts directly financial returns and information in electrical systems
planning. A promising approach to load forecasting is the Echo State Network (ESN), a …
planning. A promising approach to load forecasting is the Echo State Network (ESN), a …
Dynamical regularized echo state network for time series prediction
C Yang, J Qiao, L Wang, X Zhu - Neural Computing and Applications, 2019 - Springer
Echo state networks (ESNs) have been widely used in the field of time series prediction.
However, it is difficult to automatically determine the structure of ESN for a given task. To …
However, it is difficult to automatically determine the structure of ESN for a given task. To …
Design of sparse Bayesian echo state network for time series prediction
L Wang, Z Su, J Qiao, C Yang - Neural Computing and Applications, 2021 - Springer
Echo state network (ESN) refers to a novel recurrent neural network with a largely and
randomly generated reservoir and a trainable output layer, which has been utilized in the …
randomly generated reservoir and a trainable output layer, which has been utilized in the …
Bayesian sparse solutions to linear inverse problems with non-stationary noise with Student-t priors
A Mohammad-Djafari, M Dumitru - Digital Signal Processing, 2015 - Elsevier
Bayesian approach has become a commonly used method for inverse problems arising in
signal and image processing. One of the main advantages of the Bayesian approach is the …
signal and image processing. One of the main advantages of the Bayesian approach is the …
The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction
F Xue, Q Li, X Li - PloS one, 2017 - journals.plos.org
Recently, echo state network (ESN) has attracted a great deal of attention due to its high
accuracy and efficient learning performance. Compared with the traditional random structure …
accuracy and efficient learning performance. Compared with the traditional random structure …
Probabilistic and Bayesian networks
The Bayesian network model was introduced by Pearl in 1985 [147]. It is the best known
family of graphical models in artificial intelligence (AI). Bayesian networks are a powerful …
family of graphical models in artificial intelligence (AI). Bayesian networks are a powerful …