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 …
Recurrent stochastic configuration networks for temporal data analytics
D Wang, G Dang - arxiv preprint arxiv:2406.16959, 2024 - arxiv.org
Temporal data modelling techniques with neural networks are useful in many domain
applications, including time-series forecasting and control engineering. This paper aims at …
applications, including time-series forecasting and control engineering. This paper aims at …
Multi-reservoir echo state networks with sequence resampling for nonlinear time-series prediction
In this paper, we consider various schemes of sequence resampling in reservoir computing
models for nonlinear time series prediction. These schemes can enrich the features used for …
models for nonlinear time series prediction. These schemes can enrich the features used for …
3D-integrated multilayered physical reservoir array for learning and forecasting time-series information
A wide reservoir computing system is an advanced architecture composed of multiple
reservoir layers in parallel, which enables more complex and diverse internal dynamics for …
reservoir layers in parallel, which enables more complex and diverse internal dynamics for …
Deep liquid state machines with neural plasticity for video activity recognition
N Soures, D Kudithipudi - Frontiers in neuroscience, 2019 - frontiersin.org
Real-world applications such as first-person video activity recognition require intelligent
edge devices. However, size, weight, and power constraints of the embedded platforms …
edge devices. However, size, weight, and power constraints of the embedded platforms …
PyRCN: A toolbox for exploration and application of Reservoir Computing Networks
Abstract Reservoir Computing Networks (RCNs) belong to a group of machine learning
techniques that project the input space non-linearly into a high-dimensional feature space …
techniques that project the input space non-linearly into a high-dimensional feature space …
A cerebellum-inspired network model and learning approaches for solving kinematic tracking control of redundant manipulators
Tracking control of redundant manipulators is always a basic and important issue in robotics.
Existing studies have indicated that the pivotal region of the brain associated with human …
Existing studies have indicated that the pivotal region of the brain associated with human …
HP-ESN: Echo state networks combined with Hodrick-Prescott filter for nonlinear time-series prediction
Nonlinear time-series prediction is one of the challenging tasks in machine learning.
Recurrent neural networks and their variants have been successful in such a task owing to …
Recurrent neural networks and their variants have been successful in such a task owing to …
A hybrid control-oriented PEMFC model based on echo state networks and gaussian radial basis functions
JA Aguilar, D Chanal, D Chamagne, N Yousfi Steiner… - Energies, 2024 - mdpi.com
The goal of increasing efficiency and durability of fuel cells can be achieved through optimal
control of their operating conditions. In order to implement such controllers, accurate and …
control of their operating conditions. In order to implement such controllers, accurate and …